Consensus learning has been widely adopted in clustering tasks due to its robustness to noise and outliers, as well as its ability to aggregate diverse base results from multiple models. However, existing methods are often limited by feature alignment issues arising from heterogeneous feature dimensionalities and label permutation inconsistencies across models. To address these limitations, this paper introduces a novel Consensus Fuzzy Representation Learning (CFRL) framework. The CFRL framework initially employs various fuzzy clustering methods to generate diverse membership matrices, which are then transformed into affinity matrices to serve as base fuzzy representations. This transformation strategy not only effectively resolves feature alignment issues but also provides a unified processing mechanism for both single-view and multi-view data scenarios. To derive robust consensus features, the tensor Schatten$p$-norm encourages low-rank structure in the tensorized fuzzy representations, whereas an$l_{1}$-norm regularized error term captures and suppresses sparse noise. Moreover, a block diagonal regularizer is incorporated into the objective function, which guides the consensus feature matrix toward an optimal block diagonal structure. This structural constraint enhances cluster discriminability and enables reliable final cluster assignments. Comprehensive experimental evaluations validate that the proposed CFRL method achieves superior performance compared to state-of-the-art approaches.

Recently, single-source domain generalization (SDG) has gained popularity in medical image segmentation. As a prominent technique, adversarial image augmentation technique can generate synthetic training data that are challenging for the segmentation model to recognize. To avoid the over-augmentation problem, existing adversarial-based works often employ augmenters with relatively simple structures for medical images, typically operating at the image level, limiting the diversity of the augmented images. In this paper, we propose a Teacher-Student Instance-level Adversarial Augmentation (TSIAA) model for generalized medical image segmentation. The objective of TSIAA is to derive domain-generalizable representations by exploring out-of-source data distributions. First, we construct an Instance-level Image Augmenter (IIAG) using several Instance-level Augmentation Modules (IAMs), which are based on the learnable constrained Bèzier transformation function. Compared to image-level adversarial augmentation, instance-level adversarial augmentation breaks the uniformity of augmentation rules across different structures within an image, thereby providing greater diversity. Then, TSIAA conducts Teacher-Student (TS) learning through an adversarial approach, alternating novel image augmentation and generalized representation learning. The former delves into out-of-source and plausible data, while the latter continuously updates both the student and teacher to ensure the original and augmented features maintain consistent and generalized characteristics. By integrating both strategies, our proposed TSIAA model achieves significant improvements over state-of-the-art methods in four challenging SDG tasks. The code can be accessed at https://github.com/Wangzs0228/TSIAA.

Few-shot classification aims to develop a classifier that adapts to new tasks using only a limited number of labeled images. To overcome the limitation of lacking training images in few-shot image classification, dense features have been extensively utilized to represent images by providing more subtle and discriminative clues. However, dense feature based methods are still facing challenges despite leveraging local details in images. Primarily, these methods deal with the support set images in each category independently, which ignores the information across different categories. Furthermore, dense features suffer from background noise, when performing similarity calculations based on a large number of dense feature pairs, these methods are susceptible to interference from task-irrelevant feature pairs. In this paper, we propose a cross dense feature learning with task guidance method to address the aforementioned issues. The key components of our method include two aspects. Firstly, a dense feature extraction approach based on transformer is proposed, aiming to better utilize inter-class information within the support set. We design two types of cross-attention mechanisms to get the across information among different categories for a better representation of dense features, named Support-Support Attention (SSA) and Support-Query Attention (SQA). Secondly, a task-relevant model is trained for dense feature pairs similarity calculating, aiming to filter out feature pairs that contribute more effectively to classification. Then we can get the final similarity to predict the label of query image through summarizing weighted local similarity. The experimental results prove that our method achieves a promising improvement for few-shot classification by taking information across different categories and task attention similarity into consideration.

In this article, a double-channel event-triggered control method is developed for nonlinear uncertain interconnected systems using backstepping techniques, which introduces event-triggering mechanisms at both the sensor and controller sides. Using event-triggering mechanism at the sensor side presents a challenge to the backstepping control design as the discontinuous state/output signals received at the controller side result in nondifferentiable virtual control signals. This challenge becomes more pronounced when considering more general types of event-triggering mechanisms. Compared with existing methods, this article proposes a different idea with three innovative features: 1) the proposed event-triggering mechanism does not require the calculation of virtual control signals at the sensor side before transmitting them to the controller side; 2) the output triggering is considered directly, and there is no need to design separate controllers for the two communication scenarios without and with event-triggering, thereby avoiding the effect of errors caused by processing substitutions; and 3) it necessitates the online update of only one parameter estimator, avoiding the issue of over-parameterization. Finally, we validate the effectiveness and advantages of the proposed decentralized event-triggered control approach through a numerical case study.

This article presents a double-channel (sensor-to-controller channel and controller-to-actuator channel) event triggered control method for nonlinear interconnected systems subject to sensor and actuator faults via the backstepping technique. It should be emphasized that the utilization of triggering mechanism at the sensor side poses a challenge to the design of backstepping control, as it leads to nondifferentiable virtual control signals due to the discontinuous nature of the state/output signals received at the controller side. In contrast to existing methods, the proposed event triggering mechanism eliminates the need for computing virtual control signals at the sensor side before transmitting them to the controller side. By establishing the relationships of the corresponding variables in two communication scenarios (namely, without and with event triggering) and introducing dynamic filtering technique, the problem of nondifferentiable virtual control signals in backstepping design is solved. We present a numerical case study to validate the effectiveness and advantages of the proposed decentralized event triggered control approach.

Conventional models for hyperspectral image (HSI) classification usually demand a substantial quantity of labeled training data. However, when labeled training HSIs are unavailable or have different distributions from test HSIs, many classification models tend to exhibit significant performance declines. For the cross-scene HSI classification task, unsupervised domain adaptation (UDA) technique has been widely developed. Existing discrepancy-based or adversarial-based UDA methods may fail to learn discriminative class boundaries when large class distribution shift (CDS) exists. To alleviate this limitation, we propose the model named spatially enhanced refined classifier (SERC), which includes a coarse classifier (CC) and a refined classifier (RC). The RC constructs a memory module to fuse global-spatial and spectral information simultaneously and uses the neighborhood aggregation technique to generate the refined predictions. The refined predictions are then transferred to pseudolabels to train the CC. Thereby, a mutually reinforcing relationship between the two classifiers is established. Furthermore, we propose the class distribution match (CDM) strategy to further alleviate the serious CDS problem. Notably, SERC does not require additional training parameters, which are commonly used in existing UDA methods. Despite its simplicity, SERC achieves outstanding results. Our method has been extensively evaluated on three public HSI datasets and has shown superior performance compared with state-of-the-art (SOTA) approaches. The source code can be found athttps://github.com/Wangzs0228/SERC.

As an effective alternative to deep neural networks, broad learning system (BLS) has attracted more attention due to its efficient and outstanding performance and shorter training process in classification and regression tasks. Nevertheless, the performance of BLS will not continue to increase, but even decrease, as the number of nodes reaches the saturation point and continues to increase. In addition, the previous research on neural networks usually ignored the reason for the good generalization of neural networks. To solve these problems, this article first proposes the Extreme Fuzzy BLS (E-FBLS), a novel cascaded fuzzy BLS, in which multiple fuzzy BLS blocks are grouped or cascaded together. Moreover, the original data is input to each FBLS block rather than the previous blocks. In addition, we use residual learning to illustrate the effectiveness of E-FBLS. From the frequency domain perspective, we also discover the existence of the frequency principle in E-FBLS, which can provide good interpretability for the generalization of the neural network. Experimental results on classical classification and regression datasets show that the accuracy of the proposed E-FBLS is superior to traditional BLS in handling classification and regression tasks. The accuracy improves when the number of blocks increases to some extent. Moreover, we verify the frequency principle of E-FBLS that E-FBLS can obtain the low-frequency components quickly, while the high-frequency components are gradually adjusted as the number of FBLS blocks increases.

Real-world data tends to exhibit an inherent hierarchical structure, providing a natural multiview perspective where features at different scales can be treated as distinct views. However, most existing multiview clustering algorithms primarily focus on the inter-sample relationships at a single level. These methods overlook the hierarchical structures present in the data and are specifically designed for native multiview data. This article introduces a comprehensive multiview clustering framework that transforms both typical data and images into a unified multiview feature representation. The framework allows for extracting multiscale features from the raw data and clustering different types of data with the same algorithm. A novel scale-driven pre-processing approach unifies the feature structure across various data types and explores local relationships among samples at multiple scales. Features at larger scales delineate the global cluster contours, while features at smaller scales reveal fine-grained local details. Subsequently, the proposed method learns the view-specific partitions from different scales of views and derives consensus features through tensor low-rank representation. By optimizing these consensus features, the approach effectively captures the precise cluster shapes from coarse to fine-grained levels. The final label indicator matrix is directly obtained from these consensus features. To demonstrate the effectiveness and versatility of the proposed method, we conducted experimental comparisons with state-of-the-art (SOTA) algorithms in both multiview clustering and image segmentation across diverse datasets. The source code and datasets are released at https://github.com/ChuanbinZhang/SDTR.

Deep multiview clustering provides an efficient way to analyze the data consisting of multiple modalities and features. Recently, the autoencoder (AE)-based deep multiview clustering algorithms have attracted intensive attention by virtue of their rewarding capabilities of extracting inherent features. Nevertheless, most existing methods are still confronted by several problems. First, the multiview data usually contains abundant cross-view information, thus parallel performing an individual AE for each view and directly combining the extracted latent together can hardly construct an informative view-consensus feature space for clustering. Second, the intrinsic local structures of multiview data are complicated, hence simply embedding a preset graph constraint into multiview clustering models cannot guarantee expected performance. Third, current methods commonly utilize the Kullback-Leibler (KL) divergence as clustering loss and accordingly may yield appalling clusters that lack discriminate characters. To solve these issues, in this article we propose two new AE-based deep multiview clustering algorithms named AE-based deep multiview clustering model incorporating graph embedding (AG-DMC) and deep discriminative multiview clustering algorithm with adaptive graph constraint (ADG-DMC). In AG-DMC, a novel cross-view representation learning model is established delicately by performing decoding processes based on the cascaded view-specific latent to learn sound view-consensus features for inspiring clustering results. In addition, an entropy-regularized adaptive graph constraint is imposed on the obtained soft assignments of data to precisely preserve potential local structures. Furthermore, in the improved model ADG-DMC, the adversarial learning mechanism is adopted as clustering loss to strengthen the discrimination of different clusters for better performance. In the comprehensive experiments carried out on eight real-world datasets, the proposed algorithms have achieved superior performance in the comparison with other advanced multiview clustering algorithms.

Graffiti detection is essential in historic building protection and urban neighborhood management. Graffiti detection has made significant progress in recent years based on the development of deep learning. However, small-scale graffiti, interference from the background, and the false detection of word parts in graffiti make it a challenging problem. This article proposes a Transformer-based high-precision graffiti detection method, namely DIG. Precisely, it consists of three modules: 1) Spatial query selection (SQS), scale-aware IoU loss (SIL); 2) Denoising Task with binary contrastive denoising (BCDN); and 3) IoU-guided box denoising (IBD) modules. To detect small-scale graffiti, this paper proposes SIL to help the loss function to perceive small-scale graffiti and large-scale graffiti fairly. To reduce the false detection of word parts, this article presents the SQS module, which integrates spatial information into the query selection process of the Encoder to filter out falsely detected bounding boxes within the graffiti. To reduce the interference from the background, this article introduces a denoising task with BCDN and IBD modules, improving the model’s ability to distinguish graffiti from the background and accurately select appropriate bounding boxes. A large number of experimental results on the STORM dataset show that our method achieves state-of-the-art results with an$AP_{50}$of 87.9%. Moreover, DIG achieved competitive results on the FineFM dataset for mask detection. This indicates that DIG can also be conveniently transferred to detect other scenarios.

Sampling is commonly employed to tackle the classification of imbalanced data, with the Synthetic Minority Oversampling Technique (SMOTE) being the most widely used sampling method. In recent years, multiple variations of SMOTE, including the clustering & SMOTE based oversampling methods are proposed. Nevertheless, these methods often neglect the calculation of importance measurement for minority class samples while generating noise, boundary, or overlapping samples in some cases. To solve such problems, we propose a novel importance measurement based on sampling region information (SRI) in minority class samples by using clustering. The measurement focuses more on important samples. Based on the measurement, we also propose an oversampling method called KRISMOTE. The method not only reduces noise, but also clarifies the classification boundaries, effectively enhancing the classification performance of imbalanced data. Experimental results over the publicly available KEEL dataset demonstrate that the proposed KRISMOTE method outperforms other popular oversampling algorithms.

The dynamics of public opinion on social media affects people’s feeling and minds about international affairs and leads to the reconstruction of societal states for international conflicts. In this article, we analyze the topics’ evolution on social media during the Pelosi visit. Such kind of analysis should help the related departments sense and beware the situation effectively and efficiently, and may provide technical supports for proper policy making and responses. To facilitate this purpose, a new method is proposed and an abbreviated large-graph clustering (ALGC) algorithm has been designed to generate documents and topic representation for alleviating the overhead of high computational complexity of large graphs by reducing the dimensionality of the attention matrix and adjacency matrix. The evolution pattern of topics is also analyzed in and between different time periods. Experiment results show that the proposed method performs well, achieving a high clustering accuracy with lower computational cost. The dataset used in this article is also released for public analysis.

Knowledge question and answering (Q&A) is widely used. However, most existing semantic parsing methods in Q&A usually use cascading, which can incur error accumulation. In addition, using only one institution’s Q&A data definitely will limit the Q&A performance, while data privacy prevents sharing between institutions. This article proposes a knowledge graph-based reinforcement federated learning (KGRFL)-based Q&A approach to address these challenges. We design an end-to-end multitask semantic parsing model [MSP-bidirectional and auto-regressive transformers (BART)] that identifies question categories while converting questions into SPARQL statements to improve semantic parsing. Meanwhile, a reinforcement learning (RL)-based model fusion strategy is proposed to improve the effectiveness of federated learning, which enables multi-institution joint modeling and data privacy protection using cross-domain knowledge. In particular, it also reduces the negative impact of low-quality clients on the global model. Furthermore, a prompt learning-based entity disambiguation method is proposed to address the semantic ambiguity problem because of joint modeling. The experiments show that the proposed method performs well on different datasets. The Q&A results of the proposed approach outperform the approach of using only a single institution. Experiments also demonstrate that the proposed approach is resilient to security attacks, which is required for real applications.

The graph-information-based fuzzy clustering has shown promising results in various datasets. However, its performance is hindered when dealing with high-dimensional data due to challenges related to redundant information and sensitivity to the similarity matrix design. To address these limitations, this article proposes an implicit fuzzy k-means (FKMs) model that enhances graph-based fuzzy clustering for high-dimensional data. Instead of explicitly designing a similarity matrix, our approach leverages the fuzzy partition result obtained from the implicit FKMs model to generate an effective similarity matrix. We employ a projection-based technique to handle redundant information, eliminating the need for specific feature extraction methods. By formulating the fuzzy clustering model solely based on the similarity matrix derived from the membership matrix, we mitigate issues, such as dependence on initial values and random fluctuations in clustering results. This innovative approach significantly improves the competitiveness of graph-enhanced fuzzy clustering for high-dimensional data. We present an efficient iterative optimization algorithm for our model and demonstrate its effectiveness through theoretical analysis and experimental comparisons with other state-of-the-art methods, showcasing its superior performance.

Visual reasoning between visual images and natural language is a long-standing challenge in computer vision. Most of the methods aim to look for answers to questions only on the basis of the analysis of the offered questions and images. Other approaches treat knowledge graphs as flattened tables to search for the answer. However, there are two major problems with these works: 1) the model disregards the fact that the world we surrounding us interlinks our hearing and speaking of natural language and 2) the model largely ignores the structure of the acrlong KG. To overcome these challenging deficiencies, a model should jointly consider two modalities of vision and language, as well as the rich structural and logical information embedded in knowledge graphs. To this end, we propose a general joint representation learning framework for visual reasoning, namely, knowledge-embedded mutual guidance. It realizes mutual guidance not only between visual data and natural language descriptions but also between knowledge graphs and reasoning models. In addition, it exploits the knowledge derived from the reasoning model to boost knowledge graphs when applying the visual relation detection task. The experimental results demonstrate that the proposed approach performs dramatically better than state-of-the-art methods on two benchmarks for visual reasoning.

Unsupervised domain adaptation (UDA) aims to transfer knowledge acquired from the labeled source domain to the unlabeled target domain. However, the quality of samples can vary greatly. While partial samples are dominated by high-quality domain-invariant class-related information, others may only contain irrelevant domain-specific information or useless random noise. Treating all samples equally may lead to negative transfer, significantly impairing the performance. To address the issue of varying sample quality, we propose an attention module to emphasize the samples that are most suitable for transfer. Within the attention module, we have designed a fuzzy inference system to assess the quality of data based on its class and domain information. Such a fuzzy inference attention (FIA) module demonstrates strong interpretability due to its consideration of the fuzzy nature inherent in class and domain information within the data. FIA also has high flexibility and extensibility as the rule base can be easily adjusted by expert knowledge. More importantly, FIA does not use any parameters requiring training and has a low overhead. This makes it fast and applicable to most existing UDA methods. The experiments on several benchmark datasets prove that FIA can bring significant improvement to existing methods.

Separable nonlinear models (SNLMs) are of great importance in system modeling, signal processing, and machine learning because of their flexible structure and excellent description of nonlinear behaviors. The online identification of such models is quite challenging, and previous related work usually ignores the special structure where the estimated parameters can be partitioned into a linear and a nonlinear part. In this brief, we propose an efficient first-order recursive algorithm for SNLMs by introducing the variable projection (VP) step. The proposed algorithm utilizes the recursive least-squares method to eliminate the linear parameters, resulting in a reduced function. Then, the stochastic gradient descent (SGD) algorithm is employed to update the parameters of the reduced function. By considering the tight coupling relationship between linear parameters and nonlinear parameters, the proposed first-order VP algorithm is more efficient and robust than the traditional SGD algorithm and alternating optimization algorithm. More importantly, since the proposed algorithm just uses the first-order information, it is easier to apply it to large-scale models. Numerical results on examples of different sizes confirm the effectiveness and efficiency of the proposed algorithm.

The symbolic imagery signal decomposition is a common problem in digital signal processing. Its main purpose is to divide the symbolic imagery signal into different parts. However, in real-world applications, symbolic imagery signal captured by the camera is usually influenced by complex negative lighting environments such as highlights, reflections, drop shadows, or information loss. To overcome those problems, a generative adversarial network with Swin Transformer (StGAN) is proposed and applied to symbolic imagery signal decomposition and semantic segmentation tasks. In addition, a realistic image dataset taken in complex lighting conditions is proposed for symbolic imagery signal decomposition, which have complex lighting environments and edge information loss, we name it the MLT dataset. We demonstrate StGAN brings significant improvements in performance than some existing methods in the accuracy of symbolic imagery signal decomposition on MLT datasets. Further experiments on Sky and Facades datasets prove that StGAN works well in other tasks, especially when fewer data is involved in training, but it can still ensure good accuracy.

For the existing adaptive constrained robotic control algorithms, the demanding "feasibility conditions" on virtual controller is normally inevitable and the extra limits on constraining functions have to be imposed, making the corresponding approaches more demanding and less user friendly in control development. Here, we develop a new neuroadaptive constrained control strategy for uncertain robotic manipulators in the presence of position and velocity constraints. First, a novel unified mapping function (UMF) is constructed so that the restriction on constraining boundaries is removed and more kinds of constraining forms can be handled. Second, by integrating the UMF-based coordinate transformation with the "universal" approximation characteristic of neural networks over some compact set, the developed neuroadaptive control completely obviates the complicated yet undesired "feasibility conditions." Furthermore, it is proven that all closed-loop signals are semiglobally bounded and the constraints are not violated. The effectiveness of the proposed control is validated via a two-link rigid robotic manipulator.

The ensemble of fuzzy clustering can address the problems presented in the base clustering, such as fluctuations in results due to random initialization and performance degradation due to outliers. However, the performance of fuzzy clustering ensembles is still hampered by some challenges that include misaligned membership matrices, loss of information in the cosimilarity matrix, large storage space, unstable ensemble results due to an additional reclustering, the need for original data information for assistance, etc. To address these issues, we propose a parameter-free robust ensemble framework for fuzzy clustering. After obtaining the set of membership matrices, we cascade these membership matrices and mine the latent spectral matrix of the raw data. Benefiting from this step, we obtain global features of the dataset without knowing the specific data. Then, our framework uses transition matrices to solve the alignment problem, avoiding the storage of large-scale matrices. Most importantly, we introduce a robust weighted mechanism in the optimization model, where each base clustering is adaptively adjusted and the effect of outliers is suppressed by a robust function. In addition, the model yields the results as a membership matrix, which produces the exact partition results directly without any subsequent clustering operations. Finally, since our model is a parameter-free model, the setting of hyperparameters is avoided and the applicability of the model is improved as well. The effective algorithm of the optimization model is derived and its time complexity and convergence are analyzed. The results of competitive experiments on benchmark data show that the proposed ensemble framework is effective compared to state-of-the-art methods.

Kernel clustering has the ability to get the inherent nonlinear structure of the data. But the high computational complexity and the unknown representation of the kernel space make it unavailable for the data clustering in distributed peer-to-peer (P2P) networks. To solve this issue, we propose a new series of random feature-based collaborative kernel clustering algorithms in this article. In the most basic algorithm, each node in a distributed P2P network first maps its data into a low-dimensional random feature space with the approximation of the given kernel by using the random Fourier feature mapping method. Then, each node independently searches the clusters with its local data and the collaborative knowledge from its neighbor nodes, and the distributed clustering is performed among all network nodes until reaching the global consensus result, i.e., all nodes have the same cluster centers. In addition, an improved version is designed with assignment of feature weights, which is optimized by the maximum-entropy technique to extract important features for the cluster identification. What’s more, to relief the impact of different kernel functions and related parameters on clustering results, the combination of multiple kernels rather than a single kernel is adopted for the low-dimensional approximation, and the optimized weights are assigned to provide the guidance on the choice of the kernels and their parameters and discover significant features at the same time. Experiments on synthetic and real-world datasets show that the proposed methods achieve similar and even better results than the traditional kernel clustering methods on various performance metrics, including the average classification rate, the average normalized mutual information, and the average adjusted rand index. More importantly, the low-dimensional random features approximated to kernels and the distributed clustering mechanism adopted in these methods bring the greatly lower temporal complexity.

Categorical data are widely available in many real-world applications, and to discover valuable patterns in such data by clustering is of great importance. However, the lack of a decent quantitative relationship among categorical values makes traditional clustering approaches, which are usually developed for numerical data, perform poorly on categorical datasets. To solve this problem and boost the performance of clustering for categorical data, we propose a novel fuzzy clustering model in this article. At first, by approximating the maximum a posteriori (MAP) estimation of a discrete distribution of data partition, a new fuzzy clustering objective function is designed for categorical data. The Bayesian dissimilarity measure is formulated in this objective to tackle the subtle relationships between categorical values efficiently. Then, to further enhance the performance of clustering, a novel Kullback–Leibler divergence-based graph regularization is integrated into the clustering objective to exploit the prior knowledge on datasets, for example, the information about correlations of data points. The proposed model is solved by the alternative optimization and the experimental results on the synthetic and real-world datasets show that it outperforms the classical and relevant state-of-the-art algorithms. We also present the parameter analysis of our approach, and conduct a comprehensive study on the effectiveness of the Bayesian dissimilarity measure and the KL divergence-based graph regularization.

Unmanned Aerial Vehicles (UAV) visual tracking has always been a challenging task. Existing correlation filter tracking algorithms typically utilize the Histograms of Oriented Gradients (HOG) and Color Names (CN) method to directly incorporate the extracted target features into the model updating process. However, in low-resolution video quality, it leads to unstable target feature values. To address this limitation, we propose a novel preprocessing technique involving Gaussian denoising. This preprocessing step is designed to enhance the stability of the target’s feature values and make the target’s scale information clearer, thereby improving the tracker’s recognition capability for the target and effectively reducing noise interference. Furthermore, in contrast to other UAV trackers that rely on a singular representation of contextual information, this paper aims to enhance the utilization of historical information. Therefore, we introduce a context-based approach that integrates continuously weighted dynamic response maps from both temporal and spatial perspectives. Our tracker has the ability to adapt to rapid environmental changes during the tracking process while simultaneously reducing the potential risks of model overfitting and distortion. Extensive experiments are conducted on authoritative datasets, including DTB70, UAV123@10fps, and UAVDT, comparing our model against other advanced trackers. The experimental results validate the superior tracking performance and robustness of our tracker.

Many fuzzy subspace clustering methods have been proposed for high-dimensional image data with rich structural information. However, since these methods do not fully exploit the subspace information in each cluster, their performance on image clustering is still not promising. In this work, we propose to find soft partitions directly based on the construction of subspaces. For each cluster, we use a bilinear orthogonal subspace to represent it. Then, through the reconstruction error of a sample in the subspace corresponding to a cluster, a new membership measure for the sample to the cluster is established. Furthermore, the graph regularization is imposed on these bilinear subspaces to preserve the local relational or manifold information of the image data in the original space. Altogether, we get a clustering model considering not only the subspace information but also the manifold information in image data. An efficient optimization algorithm is proposed to our model, and its theoretical convergence and time complexity are presented correspondingly. The proposed method is a one-stage clustering model that does not require vectorized image data, thereby reducing the computational burden while maintaining the structural relationship between pixels in the image. Competitive experimental results on benchmark datasets show that our model can converge quickly with strong clustering performance, which confirms the efficiency and superiority of the proposed method compared to other state-of-the-art fuzzy clustering methods.

The traditional collaborative fuzzy clustering can effectively perform data clustering in distributed peer-to-peer networks, which is an impossible task to complete for the centralized clustering methods due to privacy and security requirements or network transmission technology constraints. But it will increase the number of clustering iterations and lead to lower efficiency of the clustering. Moreover, the collaborative mechanism hidden in the iterative process of clustering cannot be well revealed and explained. In this article, a novel series of transfer collaborative fuzzy clustering algorithms are proposed to solve these issues. In the first basic algorithm, the transfer learning among neighbor nodes vividly expresses the collaborative mechanism and enhances the information collaboration to accelerate the convergence of fuzzy clustering. Meanwhile, neighbor nodes can learn the knowledge from each other to further promote their respective clustering performance. Then, an improved version, with the learning-rate-adjustable strategy instead of fixed values, is designed to highlight the different influence between neighbor nodes, and the appropriate learning rates between neighbor nodes are achieved to ensure the stable clustering accuracy. Finally, two extended versions with the attribute-weight-entropy regularization technique are presented for the clustering of high dimensional sparse data and the extraction of important subspace features. Experiments show the efficiency of the proposed algorithms compared with the related prototype-based clustering methods.

Tensor-based robust principal component analysis (PCA) methods are efficient to discover the low-rank part of a hyperspectral image for reducing redundant information and guarantee good classification results. However, current methods cannot remove noise adequately, and the residual noise remaining in the low-rank image limits the further improvement of classification performance. Thus, enhancing the robustness to noise is important and helpful for tensor-based robust PCA (RPCA) methods to process hyperspectral images. To this end, we propose a tensor-based RPCA method with a locality preserving graph and frontal slice sparsity (LPGTRPCA) for hyperspectral image classification. Specifically, a tensor$l_{2,2,1}$norm that requires the frontal slice sparsity of a tensor is defined to extract the noise in the hyperspectral image from the frontal direction. What is more, a position-based Laplacian graph that preserves the local structures of a tensor according to the spatial position is designed for relieving the impact of the residual noise remaining in the low-rank image. Based on the tensor nuclear norm, the tensor$l_{2,2,1}$norm, and the position-based Laplacian graph, LPGTRPCA efficiently separates the low-rank part with little noise from a raw hyperspectral image and achieves more robust classification results than current methods. LPGTRPCA is optimized by the alternative direction multiplier method (ADMM), and the convergence of solutions is experimentally demonstrated. In the experiments conducted on Indian Pines, Pavia University, and Salinas datasets, LPGTRPCA outperformed various state-of-the-art and classical tensor-based RPCA methods in terms of average class classification accuracy (AA), overall classification accuracy (OA), and kappa coefficient (KC).

Environmental perception provides the necessary information for unmanned ground vehicles to recognize and interact with surrounding objects. Velodyne light detection and ranging (LiDAR) is widely used for this purpose due to its significant advantages such as high precision and being uninfluenced by varying illuminations. However, the unstructured distribution of LiDAR point clouds always affects the performance of feature extraction and object recognition. Moreover, the numbers of parameters in most deep learning models of object recognition are very large and the training process costs lots of computation consumption. This paper proposes a broad learning system (BLS) variant with a unified space autoencoder (USAE) as a lightweight model to recognize 3D objects. When the proposed method was evaluated on the LiDAR point cloud dataset and ModelNet10 dataset, the experimental results indicated that the recognition accuracy of our USAE-BLS model was similar to that of state-of-the-art 3D object recognition models. Moreover, the USAE-BLS has a much smaller model size and shorter training time than that of the deep learning models.

Deep neural networks have achieved breakthrough improvement in various application fields. Nevertheless, they usually suffer from a time-consuming training process because of the complicated structures of neural networks with a huge number of parameters. As an alternative, a fast and efficient discriminative broad learning system (BLS) is proposed, which takes the advantages of flat structure and incremental learning. The BLS has achieved outstanding performance in classification and regression problems. However, the previous studies ignored the reason why the BLS can generalize well. In this article, we focus on the interpretation from the viewpoint of the frequency domain. We discover the existence of the frequency principle in BLS, i.e., the BLS preferentially captures low-frequency components quickly and then fits the high frequencies during the incremental process of adding feature nodes and enhancement nodes. The frequency principle may be of great inspiration for expanding the application of BLS.

Many inverse problems in machine learning, system identification, and image processing include nuisance parameters, which are important for the recovering of other parameters. Separable nonlinear optimization problems fall into this category. The special separable structure in these problems has inspired several efficient optimization strategies. A well-known method is the variable projection (VP) that projects out a subset of the estimated parameters, resulting in a reduced problem that includes fewer parameters. The expectation maximization (EM) is another separated method that provides a powerful framework for the estimation of nuisance parameters. The relationships between EM and VP were ignored in previous studies, though they deal with a part of parameters in a similar way. In this article, we explore the internal relationships and differences between VP and EM. Unlike the algorithms that separate the parameters directly, the hierarchical identification algorithm decomposes a complex model into several linked submodels and identifies the corresponding parameters. Therefore, this article also studies the difference and connection between the hierarchical algorithm and the parameter-separated algorithms like VP and EM. In the numerical simulation part, Monte Carlo experiments are performed to further compare the performance of different algorithms. The results show that the VP algorithm usually converges faster than the other two algorithms and is more robust to the initial point of the parameters.

The separable nonlinear least-squares (SNLLS) problems considered in this article frequently appear in a wide range of research fields, such as machine learning, computer vision, system identification, and signal processing. The variable projection algorithm proposed by Golub and Pereyra, which reduces the dimension of the parameters by projecting the linear parameters out of the problem, is quite valuable in solving SNLLS problems. Previous implementations of the variable projection algorithm are based on matrix factorization. In this article, we propose an iterative implementation of the variable projection algorithm. Compared with previous implementations based on matrix decomposition, the proposed method can effectively avoid suffering from large condition number of the matrix or even matrix decomposition failure when dealing with ill-posed SNLLS problems. Numerical experiments on real-world data and synthetic data show the efficiency and robustness of the proposed iterative variable projection algorithm.

In this article, the problem of constant yet deferred output constraint for uncertain strict-feedback nonlinear systems is studied. By “deferred output constraint,” we mean that the system output is free/released from any constraint in the initial interval and then preserves within a bounded region right after a finite time. Due to such a form of output constraint, the normally employed Barrier Lyapunov Function (BLF)-based results become invalid because the corresponding BLF is undefined in the initial period. The problem will be rather complicated yet challenging if computation and communication constraints are taken into account. By developing an error-based nonlinear function and constructing a prescribed-time scaling function, together with the approximate ability of neural networks, a varying threshold-based event-triggering adaptive neural control algorithm is presented such that not only the deferred output constraint can be ensured and the network resources can be saved but also the tracking error is able to converge to a pregiven region in a prescribed time. Simulations are provided to demonstrate the effectiveness of the proposed control.

Unbalanced geometric structure caused by variations with deformations, rotations and outliers is a critical issue that hinders correspondence establishment between image pairs in existing graph matching methods. To deal with this problem, in this work, we propose a dual calibration mechanism (DCM) for establishing feature points correspondence in graph matching. In specific, we embed two types of calibration modules in the graph matching, which model the correspondence relationship in point and edge respectively. The point calibration module performs unary alignment over points and the edge calibration module performs local structure alignment over edges. By performing the dual calibration, the feature points correspondence between two images with deformations and rotations variations can be obtained. To enhance the robustness of correspondence establishment, the L2,p-norm is employed as the similarity metric in the proposed model, which is a flexible metric due to setting the different p values. Finally, we incorporate the dual calibration and L2,p-norm based similarity metric into the graph matching model which can be optimized by an effective algorithm, and theoretically prove the convergence of the presented algorithm. Experimental results in the variety of graph matching tasks such as deformations, rotations and outliers evidence the competitive performance of the presented DCM model over the state-of-the-art approaches.

Self-representation methods, such as low-rank representation (LRR), sparse subspace clustering (SSC) and their variants may generate negative coding coefficients since there is no explicit nonnegative constraint. These negative coefficients lack physical meaning. To be specific, it is unreasonable to allow a query sample encoded by heterogeneous samples to cancel each other out with subtractions. In this paper, we propose a novel model named Laplacian regularized nonnegative representation (LapNR). The new model improves its physical interpretability by ensuring that the query sample should be approximated from homogeneous samples and irrelevant to heterogeneous ones. More importantly, it captures the geometric information of input data by imposing the graph Laplacian to the nonnegative representations. As a result, the representation matrix generated by our LapNR model becomes sparse and discriminative. Based on the alternating direction method of multipliers (ADMM), an efficient optimization procedure is developed for LapNR. The extensive experiments on clustering and dimensionality reduction tasks show the effectiveness and efficiency of our LapNR.

For pure-feedback nonlinear systems under asymmetric output constraint, we present a low-cost neuroadaptive tracking control solution with salient features benefited from two design steps. In the first step, a novel output-dependent universal barrier function (ODUBF) is constructed such that not only the restrictive condition on constraining boundaries/functions is removed but also both constrained and unconstrained cases can be handled uniformly without the need for changing the control structure. In the second step, to reduce the computational burden caused by the neural network (NN)-based approximators, a single parameter estimator is developed so that the number of adaptive law is independent of the system order and the dimension of system parameters, making the control design inexpensive in computation. Furthermore, it is shown that all signals in the closed-loop system are semiglobally uniformly ultimately bounded, the tracking error converges to an adjustable neighborhood of the origin, and the violation of output constraint is prevented. The effectiveness of the proposed method can be validated via numerical simulation.

Mining the relationship structures among the investors plays a vital role in promoting economic development as well as preventing financial risks, especially in the context of big data. This article proposes fast networking approaches from investment big data to explore three underlying structures, namely, investment pedigrees, investment groups, and structural holes. Inspired by disjoint sets and path compression, we first present a pedigree classification algorithm to identify investment pedigrees. Second, through introducing a pruning strategy and a data structure termed as “2-tuple list,” we develop a novel linear-time structure mining algorithm in network (SMAN) for investigating investment groups and structural holes from the investment pedigree. Finally, we show that our SMAN has higher clustering accuracy and efficiency than other existing algorithms on a variety of real-world tasks in terms of normalized mutual information (NMI) values. Our method is particularly well suited for mining the underlying structures from investment big data.

Automatic real-time emotion recognition based on multi-channel EEG signals is a significant and challenging task in neurology and psychiatry. In recent years, deep learning has been used in EEG emotion recognition. However, many existing deep learning based methods still require complex pre-processing or additional feature extraction, which make it difficult to achieve real-time emotion recognition. In this paper, an end-to-end model named Temporal Convolutional Broad Learning System (TCBLS) was designed for multi-channel EEG based emotion recognition. The TCBLS takes one-dimensional EEG signals as input, then extracts emotion-related features of EEG automatically. In this model, the Temporal Convolutional Network (TCN) is designed to extract EEG temporal features and deep abstract features simultaneously, then Broad Learning System (BLS) is used to map the features to a more discriminative space and further enhance the features. We evaluated our method on DEAP database, performing 10-fold cross-validation on each subject to obtain the classification accuracy. Experimental results indicate that the performance of TCBLS is better than other comparison methods, and the mean accuracy of TCBLS is 99.5755% and 99.5781% on valence and arousal classification task respectively. The results demonstrate the effectiveness and robustness of TCBLS in EEG emotion recognition.

Fuzzy clustering is a classical approach to provide the soft partition of data. Although its enhancements have been intensively explored, fuzzy clustering still suffers from the difficulties in handling real high-dimensional data with complex latent distribution. To solve the problem, this article proposes a deep fuzzy clustering method by representing the data in a feature space produced by the deep neural network. From the perspective of representation learning, three constraints or objectives are imposed to the neural network to enhance the clustering-friendly representation. At first, as a good representation of data, the mapped data in the new feature space should support the reconstruction of original data. So, the autoencoder architecture is applied to ensure that the original data can be recovered by decoding the encoded representation with another neural network. Second, to solve the clustering problem efficiently, the intracluster compactness and the intercluster separability are to be minimized and maximized, respectively, in the new feature space. At last, considering that the data in the same class should be close to each other, the affinities between new representations are tuned in accordance with the discriminative information. Altogether, we design a graph-regularized deep normalized fuzzy compactness and separation clustering model to conduct representation learning and soft clustering simultaneously. The learning algorithm based on stochastic gradient descent is proposed to the model, and the comparative studies with baseline clustering algorithms on real-world data illustrate the superiority of the proposal.

Fuzzy clustering generates a membership vector for each data point in the dataset to indicate its belongingness to different clusters. This procedure can be regarded as an encoding process and the obtained vectors of memberships are the new representations of original data. Naturally, the affinities between new representations or the vectors of memberships should be consistent with the ones between original data points. For example, the data points close to each other should also take similar membership vectors. Such constraints on the affinities of memberships are valuable prior knowledge that should be imposed to the objective function of fuzzy clustering for better performance. To this end, we introduce the membership affinity lasso for fuzzy clustering in this paper. Utilizing alternating direction method of multipliers, an efficient approach is derived to optimize the general membership affinity lasso regularized fuzzy clustering model in offline manner. As illustrative examples, three new fuzzy clustering algorithms with the membership affinity lasso are proposed. Experiments on the synthetic and real data demonstrate the superiority and flexibility of the proposed algorithms.

Since data are collected from a range of sources via different techniques, multiview clustering has become an emerging technique for unsupervised data classification. However, most existing soft multiview clustering methods only consider the pairwise correlations and ignore high-order correlations among multiple views. To integrate more comprehensive information from different views, this article innovates a fuzzy clustering model using the low-rank tensor to address the multiview data clustering problem. Our method first conducts a standard fuzzy clustering on different views of the data separately. Then, the obtained soft partition results are aggregated as the new data to be handled by a Kullback-Leibler (KL) divergence-based fuzzy model with low-rank tensor constraints. The KL divergence function, which replaces the traditional minimized Euclidean distance, can enhance the robustness of the model. More importantly, we formulate fuzzy partition matrices of different views as a third-order tensor. So, a low-rank tensor is introduced as a norm constraint in the KL divergence-based fuzzy clustering to obtain dexterously high-order correlations of different views. The minimization of the final model is convex and we present an efficient augmented Lagrangian alternating direction method to handle this problem. Specially, the global membership is derived by using tensor factorization. The efficiency and superiority of the proposed approach are demonstrated by the comparison with state-of-the-art multiview clustering algorithms on many multiple-view data sets.

In this paper, we consider the term selection problem for a class of separable nonlinear models. The strategy is a two-step process in which the nonlinear parameters of the model are first optimized by a variable projection method, and then the least absolute shrinkage and selection operator are adopted to obtain a sparse solution by picking out the critical terms automatically. This process may be repeated several times. The proposed algorithm is tested on parameter estimation problems for an exponential model and a neural network-based model. The numerical results show that the proposed algorithm can pick out the appropriate terms from the overparameterized model and the obtained parsimonious model performs better than other methods.

BACKGROUND: Mass spectra are usually acquired from the Liquid Chromatography-Mass Spectrometry (LC-MS) analysis for isotope labeled proteomics experiments. In such experiments, the mass profiles of labeled (heavy) and unlabeled (light) peptide pairs are represented by isotope clusters (2D or 3D) that provide valuable information about the studied biological samples in different conditions. The core task of quality control in quantitative LC-MS experiment is to filter out low-quality peptides with questionable profiles. The commonly used methods for this problem are the classification approaches. However, the data imbalance problems in previous control methods are often ignored or mishandled. In this study, we introduced a quality control framework based on the extreme gradient boosting machine (XGBoost), and carefully addressed the imbalanced data problem in this framework. RESULTS: In the XGBoost based framework, we suggest the application of the Synthetic minority over-sampling technique (SMOTE) to re-balance data and use the balanced data to train the boosted trees as the classifier. Then the classifier is applied to other data for the peptide quality assessment. Experimental results show that our proposed framework increases the reliability of peptide heavy-light ratio estimation significantly. CONCLUSIONS: Our results indicate that this framework is a powerful method for the peptide quality assessment. For the feature extraction part, the extracted ion chromatogram (XIC) based features contribute to the peptide quality assessment. To solve the imbalanced data problem, SMOTE brings a much better classification performance. Finally, the XGBoost is capable for the peptide quality control. Overall, our proposed framework provides reliable results for the further proteomics studies.

An innovative quantification method for 18O labeled LC-MS data is proposed based on I-Ching divination evolutionary algorithm(IDEA). Considering label efficiency for calculating the least squares regression function, traditional methods based on genetic algorithm(GA) or other optimized algorithms will bring high level of computation complexity. The proposed method applies very flexible I-Ching operators(ICOs)— intrication operator, turnover operator, and mutual operator. The objective is the function of determining coefficients, which include the 18O/16O ratio r, the label efficiency f, and the abundance a of 16O. Comparing with GA, the proposed algorithm can significantly improve the accuracy and precision of peptide ratio measurements and better performs in the evolution procedure over mathematically calculating the function. Simultaneously we run the experiment with mix peptide raw data of predefined ratio. The result shows that our proposal algorithm is superior to the conventional GA in exploring optimum solution for better quantification accuracy.

For a class of nonlinear least squares problems, it is usually very beneficial to separate the variables into a linear and a nonlinear part and take full advantage of reliable linear least squares techniques. Consequently, the original problem is turned into a reduced problem which involves only nonlinear parameters. We consider in this paper four separated algorithms for such problems. The first one is the variable projection (VP) algorithm with full Jacobian matrix of Golub and Pereyra. The second and third ones are VP algorithms with simplified Jacobian matrices proposed by Kaufman and Ruano et al. respectively. The fourth one only uses the gradient of the reduced problem. Monte Carlo experiments are conducted to compare the performance of these four algorithms. From the results of the experiments, we find that: 1) the simplified Jacobian proposed by Ruano et al. is not a good choice for the VP algorithm; moreover, it may render the algorithm hard to converge; 2) the fourth algorithm perform moderately among these four algorithms; 3) the VP algorithm with the full Jacobian matrix perform more stable than that of the VP algorithm with Kuafman's simplified one; and 4) the combination of VP algorithm and Levenberg-Marquardt method is more effective than the combination of VP algorithm and Gauss-Newton method.

Recently, locality-constrained linear coding (LLC) has been drawn great attentions and been widely used in image processing and computer vision tasks. However, the conventional LLC model is always fragile to outliers. In this paper, we present a robust locality-constrained bi-layer representation model to simultaneously hallucinate the face images and suppress noise and outliers with the assistant of a group of training samples. The proposed scheme is not only able to capture the nonlinear manifold structure but also robust to outliers by incorporating a weight vector into the objective function to subtly tune the contribution of each pixel offered in the objective. Furthermore, a high-resolution (HR) layer is employed to compensate the missed information in the low-resolution (LR) space for coding. The use of two layers (the LR layer and the HR layer) is expected to expose the complicated correlation between the LR and HR patch spaces, which helps to obtain the desirable coefficients to reconstruct the final HR face. The experimental results demonstrate that the proposed method outperforms the state-of-the-art image super-resolution methods in terms of both quantitative measurements and visual effects.

This paper is to design substitution boxes (S-Boxes) using innovative I-Ching operators (ICOs) that have evolved from ancient Chinese I-Ching philosophy. These three operators-intrication, turnover, and mutual- inherited from I-Ching are specifically designed to generate S-Boxes in cryptography. In order to analyze these three operators, identity, compositionality, and periodicity measures are developed. All three operators are only applied to change the output positions of Boolean functions. Therefore, the bijection property of S-Box is satisfied automatically. It means that our approach can avoid singular values, which is very important to generate S-Boxes. Based on the periodicity property of the ICOs, a new network is constructed, thus to be applied in the algorithm for designing S-Boxes. To examine the efficiency of our proposed approach, some commonly used criteria are adopted, such as nonlinearity, strict avalanche criterion, differential approximation probability, and linear approximation probability. The comparison results show that S-Boxes designed by applying ICOs have a higher security and better performance compared with other schemes. Furthermore, the proposed approach can also be used to other practice problems in a similar way.

Uncertain data clustering has been recognized as an essential task in the research of data mining. Many centralized clustering algorithms are extended by defining new distance or similarity measurements to tackle this issue. With the fast development of network applications, these centralized methods show their limitations in conducting data clustering in a large dynamic distributed peer-to-peer network due to the privacy and security concerns or the technical constraints brought by distributive environments. In this paper, we propose a novel distributed uncertain data clustering algorithm, in which the centralized global clustering solution is approximated by performing distributed clustering. To shorten the execution time, the reduction technique is then applied to transform the proposed method into its deterministic form by replacing each uncertain data object with its expected centroid. Finally, the attribute-weight-entropy regularization technique enhances the proposed distributed clustering method to achieve better results in data clustering and extract the essential features for cluster identification. The experiments on both synthetic and real-world data have shown the efficiency and superiority of the presented algorithm.

The random Fourier Features method has been found very effective in approximating the kernel functions. Our former studies show that through a mixing mechanism of the feature space formed by random Fourier features and certain linear algorithms, the fuzzy clustering results in the approximated feature space are comparable to or even exceed the classical kernel-based algorithms. To increase the robustness of clustering results over outliers, this paper proposes to employ the shadowed C-Means algorithm in the approximated kernel feature space generated by the random Fourier Features method. The experiments compared with traditional fuzzy C-Means algorithm demonstrate the strengths and efficiency of the proposed approach.

An innovative simulated evolutionary algorithm (EA), called I-Ching divination EA (IDEA), and its convergence analysis are proposed and investigated in this paper. Inherited from ancient Chinese culture, I-Ching divination has always been used as a divination system in traditional and modern China. There are three operators evolved from I-Ching transformations in this new optimization algorithm, intrication operator, turnover operator, and mutual operator. These new operators are very flexible in the evolution procedure. Additionally, two new spaces are defined in this paper, which are denoted as hexagram space and state space. In order to analyze the convergence property of I-Ching divination algorithm, Markov model was adopted to analyze the characters of the operators. Meanwhile, the proposed algorithm is proved to be a homogeneous Markov chain with the positive transition matrix. After giving some basic concepts of necessary theorems, definition of admissible functions and I-Ching map, a precise proof of the states converge to the global optimum is presented. Compared with the genetic algorithm, particle swarm optimization, and differential evolution algorithm, our proposed IDEA is much faster in reaching the global optimum.

Joint sparse representation (JSR) has shown great potential in various image processing and computer vision tasks. Nevertheless, the conventional JSR is fragile to outliers. In this paper, we propose a weighted JSR (WJSR) model to simultaneously encode a set of data samples that are drawn from the same subspace but corrupted with noise and outliers. Our model is desirable to exploit the common information shared by these data samples while reducing the influence of outliers. To solve the WJSR model, we further introduce a greedy algorithm called weighted simultaneous orthogonal matching pursuit to efficiently approximate the global optimal solution. Then, we apply the WJSR for mixed noise removal by jointly coding the grouped nonlocal similar image patches. The denoising performance is further improved by incorporating it with the global prior and the sparse errors into a unified framework. Experimental results show that our denoising method is superior to several state-of-the-art mixed noise removal methods.

This paper focuses on the leader-following consensus control problem of multiagent systems in random vibration environment. The Itô stochastic systems with heterogeneous unknown dynamics and external disturbances are established to describe the agents in random vibration environment. The fuzzy logic systems are applied to approximate the unknown nonlinear dynamics, and one adaptive parameter is designed to decay the effect of external disturbances. We present a new distributed consensus controller for each follower agent only based on local information that is measured or received from its neighbors and itself. Under the consensus controller, we prove that all the follower agents can keep consensus with the leader, even though only a very small part of follower agents can measure or receive the state information of the leader. Furthermore, the states of all the follower agents are bounded in probability. Finally, the simulation results are provided to illustrate the effectiveness of the designed algorithm.

This paper puts forward a systems approach for the parallel control and management of the high-speed maglev system (HMS). An artificial HMS is first established by using a multiagent-based technique, and we demonstrate its consistence with the actual HMS. We then conduct some computational experiments and summarize some operational rules for the artificial HMS. Finally, the parallel control and management for the HMS are achieved by parallel execution of the artificial and actual HMSs with parallel interactions between them. We evaluate our approach overall by ensuring the safety and reliability of the HMS through parallel control and management. The solutions and recommendations for the safety control and effective management of the HMS can be provided by the proposed approach.

For a high-speed train (HST), quick and accurate localization of its position is crucial to safe and effective operation of the HST. In this paper, we develop a mathematical localization model by analyzing the location report created by the HST. Then, we apply two sparse optimization algorithms, i.e., iterative pruning error minimization (IPEM) and L0-norm minimization algorithms, to improve the sparsity of both least squares support vector machine (LSSVM) and weighted LSSVM models. Furthermore, in order to enhance the adaptability and real-time performance of established localization model, four online sparse learning algorithms LSSVM-online, IPEM-online, L0-norm-online, and hybrid-online are developed to sparsify the training data set and update parameters of the LSSVM model online. Finally, the field data of the Beijing-Shanghai highspeed railway (BS_HSR) is used to test the performance of the established localization models. The proposed method overcomes the problem of memory constraints and high computational costs resulting in highly sparse reductions to the LSSVM models. Experiments on real-world data sets from the BS_HSR illustrate that these methods achieve sparse models and increase the realtime performance in online updating process on the premise of reducing the location error. For the rapid convergence of proposed online sparse algorithms, the localization model can be updated when the HST passes through the balise every time.

Automatic anomaly detection is of great importance in the big data era because the large volume of raw data can be accessed easily and the automatic method to analyze the data is desirable. This paper uses a framework based on fuzzy c-means clustering to detect anomaly in temporal traffic data. In this framework the sliding window is employed first to generate a collection of segments or subsequences of the time series. Then the fuzzy clustering is applied on those segments to reveal the outliers or abnormal segments in the series. The abnormal score for each segment is calculated according to the clustering results. To obtain the best setting of parameters and more meaningful abnormal scores, we design one novel performance index. The proposed approach is tested on the temporal traffic data set collected from Beijing, China, and the results demonstrate that the proposed approach can identify many valuable traffic patterns in the data.

Fuzzy clustering methods are efficient tools for image segmentation. However, most of fuzzy clustering approaches are too sensitive to deal with the misclassification of pixels in image segmentation. In recent years, a variety of enhanced fuzzy clustering approaches have been proposed to obtain smoother results in noised image segmentation, but usually with less accurate edges in these results. To fix this problem, we derive some modified algorithms by using Guided Filter, the filter that can reserve the edge information when smoothing every region in segmentation. This paper provides a new roadmap for the application of Guided Filter and gives a thorough discussion of its applications in classical clustering methods, which are Fuzzy C-Means (FCM) and some other variants of FCM. Verified by the experimental results, we conduct a good use of Guided Filter to improve the performance of fuzzy clustering methods in a simple way.

Region-based image fusion methods have a number of advantages over pixel-based image fusion methods. In this paper, we propose a region-based multi-focus image fusion approach using guided filtering and greedy analysis. The original images are enhanced by guided filter first and then we conduct the sparse representation of images using the greedy algorithm. Here, simultaneously orthogonal matching pursuit (SOMP) algorithm is adopted, which could obtain more accurate sparse coefficients under the same basis by processing the source image simultaneously. In order to form the regional map, the clarity enhanced image is designed and normalized cuts algorithm is adopted to segment it. According to the regional fused sparse coefficients, we recover the fused image. To verify the effectiveness of the proposed method, several pairs of multi-focus images are tested. Comparing with other fusion methods, the experiment results demonstrate that the performance of multifocus image fusion by our proposed method is superior.

We propose a gradient radial basis function based varying-coefficient autoregressive (GRBF-AR) model for modeling and predicting time series that exhibit nonlinearity and homogeneous nonstationarity. This GRBF-AR model is a synthesis of the gradient RBF and the functional-coefficient autoregressive (FAR) model. The gradient RBFs, which react to the gradient of the series, are used to construct varying coefficients of the FAR model. The Mackey-Glass chaotic time series are used to evaluate the performance of the proposed method. It is shown that the GRBF-AR model not only achieves much more parsimonious structure but also much better prediction performance than that of GRBF network.

In recent years, deep learning caves out a research wave in machine learning. With outstanding performance, more and more applications of deep learning in pattern recognition, image recognition, speech recognition, and video processing have been developed. Restricted Boltzmann machine (RBM) plays an important role in current deep learning techniques, as most of existing deep networks are based on or related to it. For regular RBM, the relationships between visible units and hidden units are restricted to be constants. This restriction will certainly downgrade the representation capability of the RBM. To avoid this flaw and enhance deep learning capability, the fuzzy restricted Boltzmann machine (FRBM) and its learning algorithm are proposed in this paper, in which the parameters governing the model are replaced by fuzzy numbers. This way, the original RBM becomes a special case in the FRBM, when there is no fuzziness in the FRBM model. In the process of learning FRBM, the fuzzy free energy function is defuzzified before the probability is defined. The experimental results based on bar-and-stripe benchmark inpainting and MNIST handwritten digits classification problems show that the representation capability of FRBM model is significantly better than the traditional RBM. Additionally, the FRBM also reveals better robustness property compared with RBM when the training data are contaminated by noises.

Many impulse noise (IN) reduction methods suffer from two obstacles, the improper noise detectors and imperfect filters they used. To address such issue, in this paper, a weighted couple sparse representation model is presented to remove IN. In the proposed model, the complicated relationships between the reconstructed and the noisy images are exploited to make the coding coefficients more appropriate to recover the noise-free image. Moreover, the image pixels are classified into clear, slightly corrupted, and heavily corrupted ones. Different data-fidelity regularizations are then accordingly applied to different pixels to further improve the denoising performance. In our proposed method, the dictionary is directly trained on the noisy raw data by addressing a weighted rank-one minimization problem, which can capture more features of the original data. Experimental results demonstrate that the proposed method is superior to several state-of-the-art denoising methods.

In order to apply successfully the fuzzy clustering algorithms like shadowed C-means (SCM) to image segmentation problems, the spatial information related with each pixel in the image should be carefully calculated and appended to the clustering algorithms. In this paper, the non-local spatial information calculation is introduced to SCM. Because the data in the kernel space demonstrate more linearly-separable shape and the distances calculated in it shows the property of robust to noise and outliers, the proposed clustering algorithm is conducted in the kernel space (aka feature space) mapped from the original space by some implicit mapping functions defined in the kernel functions. Simulations results on some noise images and the comparison with traditional methods demonstrate the efficiency and superiority of the proposed new approach.

For the single kernel based clustering methods, the selection of kernel parameters largely affects the clustering results. To address this issue, a new multiple kernel fuzzy c-means clustering algorithm is proposed, in which the maximum entropy method is used to regularize the kernel weights and decide the important kernels. A new objective function is developed to simultaneously minimize the within cluster dispersion in the kernel space and maximize the kernel-weight-entropy. Thus, the optimal clustering results have been yielded and the important kernels are extracted according to the optimal assignment of kernel weights. Experiments on synthetic ‘nonspherical’ shaped datasets have demonstrated the efficiency and superiority of the presented algorithms.

Due to privacy and security requirements or technical constraints, traditional centralized approaches to data clustering in a large dynamic distributed peer-to-peer network are difficult to perform. In this paper, a novel collaborative fuzzy clustering algorithm is proposed, in which the centralized clustering solution is approximated by performing distributed clustering at each peer with the collaboration of other peers. The required communication links are established at the level of cluster prototype and attribute weight. The information exchange only exists between topological neighboring peers. The attribute-weight-entropy regularization technique is applied in the distributed clustering method to achieve an ideal distribution of attribute weights, which ensures good clustering results. And the important features are successfully extracted for the high-dimensional data clustering. The kernelization of the proposed algorithm is also realized as a practical tool for clustering the data with “nonspherical”-shaped clusters. Experiments on synthetic and real-world datasets have demonstrated the efficiency and superiority of the proposed algorithms.

A traditional neuro-fuzzy system is transformed into an equivalent fully connected three layer neural network (NN), namely, the fully connected neuro-fuzzy inference systems (F-CONFIS). The F-CONFIS differs from traditional NNs by its dependent and repeated weights between input and hidden layers and can be considered as the variation of a kind of multilayer NN. Therefore, an efficient learning algorithm for the F-CONFIS to cope these repeated weights is derived. Furthermore, a dynamic learning rate is proposed for neuro-fuzzy systems via F-CONFIS where both premise (hidden) and consequent portions are considered. Several simulation results indicate that the proposed approach achieves much better accuracy and fast convergence.

Image segmentation plays an important role in medical imaging for clinical purposes. In this paper, an image segmentation method using the ensemble of fuzzy clustering is proposed, in which we classify the pixels in an image according to heterogeneous clustering methods, and then combine the clustering results by a KL-Divergence based fuzzy clustering algorithm to provide the final image segmentation results. Experimental results show that the proposed method performs better than some existing clustering-based methods in medical image segmentation problems.

Traffic-monitoring can efficiently promote better urban planning and encourage better use of public transport. The investment of traffic-monitoring system will bring huge social and economic benefits by reducing congestion and pollution. Based on the wireless sensor network (WSN) technique, this paper investigates the problem of efficiently monitoring, collecting, and disseminating traffic information in an urban setting. We design the architecture of WSN-based traffic-monitoring system and specify the phases of the traffic information acquisition and delivery in the context of WSN environment. A novel data-centric routing algorithm is proposed for data delivery, in which multiple routing-related information are adopted for routing decision making. Simulation results have shown the good performance of the proposed routing scheme compared with other traditional schemes.

This paper introduces some new image segmentation methods in the framework of shadowed c-means clustering. By implanting the local and non-local spatial information in the membership value estimation procedure, we propose the Local Spatial Shadowed C-Means (LSSCM) algorithm, Non-local Spatial Shadowed C-Means (NLSSCM) algorithm and their combination - L+NLSSCM. Compared to traditional fuzzy c-means and shadowed c-means based approaches, the proposed image segmentation algorithms can obtain better segmentation results on test images. It is observed the proposed algorithms can effectively tackle the overlapping among segments and the noise problem in images.

Traffic monitoring can efficiently promote urban planning and encourage better use of public transport. Efficient traffic information collection is one important part of traffic monitoring systems. Based on a technique using wireless sensor networks (WSNs), this paper provides a flexible framework for regional traffic information collection in accordance with user request. This framework serves as a basis for future research in designing and implementing traffic monitoring applications. A two-layer network architecture is established for traffic information acquisition in the context of a WSN environment. In addition, a user-customizable data-centric routing scheme is proposed for traffic information delivery, in which multiple routing-related information is considered for decision-making to meet different user requirements. Simulations have shown good performance of the proposed routing scheme compared with other traditional routing schemes on a real-world urban traffic network.

Proximity-based fuzzy c-means algorithm (P-FCM), a classical semi-supervised clustering algorithm, concerns with the number of proximity “hints” or constraints that specify an extent to which some pairs of instances are considered similar or. By replacing the fuzzy c-means in P-FCM with a kernel fuzzy c-means, this paper proposes a new semi-supervised clustering algorithm named proximity-based kernel fuzzy c-means (PKFCM), which not only can cluster non-linearly separable data but also can utilize the user inputs about proximity among data to guide the clustering. In addition, PKFCM is able to apply the user inputs to select decent parameters for kernel functions. Simulations on some synthetic data demonstrate the feasibility and advantages of proposed PKFCM.

A novel quantification method is proposed for labeled LC-MS data. Unlike the traditional extracted ion chromatogram based quantification methods, the new method considers the reliability or the quality of signals with different intensities and applies more weights to the more reliable signals in the relative quantification procedure. Testing shows that the new method has improved accuracy than current quantification software.

BACKGROUND: Identifying corresponding features (LC peaks registered by identical peptides) in multiple Liquid Chromatography/Mass Spectrometry (LC-MS) datasets plays a crucial role in the analysis of complex peptide or protein mixtures. Warping functions are commonly used to correct the mean of elution time shifts among LC-MS datasets, which cannot resolve the ambiguity of corresponding feature identification since elution time shifts are random. We propose a Statistical Corresponding Feature Identification Algorithm(SCFIA) based on both elution time shifts and peak shape correlations between corresponding features. SCFIA first trains a set of statistical models, and then, all candidate corresponding features are scored by the statistical models to find the maximum likelihood solution. RESULTS: We test SCFIA on publicly available datasets. We first compare its performance with that of warping function based methods, and the results show significant improvements. The performance of SCFIA on replicates datasets and fractionated datasets is also evaluated. In both cases, the accuracy is above 90%, which is near optimal. Finally the coverage of SCFIA is evaluated, and it is shown that SCFIA can find corresponding features in multiple datasets for over 90% peptides identified by Tandem MS. CONCLUSIONS: SCFIA can be used for accurate corresponding feature identification in LC-MS. We have shown that peak shape correlation can be used effectively for improving the accuracy. SCFIA provides high coverage in corresponding feature identification in multiple datasets, which serves the basis for integrating multiple LC-MS measurements for accurate peptide quantification.

In this paper, a generalized multiple-kernel fuzzy C-means (FCM) (MKFCM) methodology is introduced as a framework for image-segmentation problems. In the framework, aside from the fact that the composite kernels are used in the kernel FCM (KFCM), a linear combination of multiple kernels is proposed and the updating rules for the linear coefficients of the composite kernel are derived as well. The proposed MKFCM algorithm provides us a new flexible vehicle to fuse different pixel information in image-segmentation problems. That is, different pixel information represented by different kernels is combined in the kernel space to produce a new kernel. It is shown that two successful enhanced KFCM-based image-segmentation algorithms are special cases of MKFCM. Several new segmentation algorithms are also derived from the proposed MKFCM framework. Simulations on the segmentation of synthetic and medical images demonstrate the flexibility and advantages of MKFCM-based approaches.

In this paper, multiple kernel fuzzy c-means is introduced as a general framework for image segmentation problem. Multiple kernel fuzzy c-means provides us a new approach to combine different information of image pixels in segmentation algorithms. That is, different information of image pixels are combined in the kernel space by combining different kernel functions defined on specific information domains. Two new segmentation algorithms are derived from the proposed framework. Simulations on the segmentation of synthetic and medical images demonstrate the flexibility and advantages of multiple kernel fuzzy c-means based approaches.

To solve the classification problems more adaptively and accurately, this paper studies the parametric t-norm and s-norm based fuzzy classification systems, where the fuzzy decision tree provides fuzzy rules and the system sensitivity on historical data works as a feedback controller. The system sensitivity with respect to the parameters of parametric t-norm and s-norm is investigated as the vehicle to reveal the regulation rules between the parameters and the system output. Based on the simulation results on several UCI data sets and the analysis of the system sensitivity, this paper provides some valuable regulation rules between the system output and the parameters of t-norm and s-norm for data with different characteristics. As the feedback controller, the system sensitivity, which is used to tune the parameters for unknown samples, improves the system's efficiency a lot.

Linguistic interface is a group of linguistic terms or fuzzy descriptions that describe variables in a system utilizing corresponding membership functions. Its transparency completely or partly decides the interpretability of fuzzy models. This paper proposes a GRadiEnt-descEnt-based Transparent lInguistic iNterface Generation (GREETING) approach to overcome the disadvantage of traditional linguistic interface generation methods where the consideration of the interpretability aspects of linguistic interface is limited. In GREETING, the widely used interpretability criteria of linguistic interface are considered and optimized. The numeric experiments on the data sets from University of California, Irvine (UCI) machine learning databases demonstrate the feasibility and superiority of the proposed GREETING method. The GREETING method is also applied to fuzzy decision tree generation. It is shown that GREETING generates better transparent fuzzy decision trees in terms of better classification rates and comparable tree sizes.

Linguistic terms are widely used in fuzzy modeling. The generation of membership functions for the linguistic terms is usually done by fuzzy C-means algorithm (FCM). However, most of FCM-based membership function generation algorithms consider little on the transparency or the understandability of the resulting membership functions. This paper proposes a gradient pre-shaped fuzzy C-means (GradPFCM) algorithm to generate better transparent membership functions. GradPFCM will preserve the predefined transparent shapes of membership functions during the process of the gradient descent based optimization of the clustering algorithm. Numeric experiments based on data collected in a civil project demonstrate the feasibility and superiority of the proposed new algorithm.

The linguistic interface is a group of fuzzy sets and their corresponding membership functions applied to define the linguistic terms used in fuzzy modeling, granular computing and computing with words. The fuzzy C-means algorithm (FCM) is widely used in the generation of membership functions for linguistic terms from historical data. However, most of FCM-based membership function generation algorithms and their variants consider little on the transparency or the understandability of the resulting membership functions. This paper proposes a genetic pre-shaped fuzzy C-means algorithm (GPFCM) to generate transparent membership functions for linguistic terms in linguistic interfaces. The proposed algorithm will preserve predefined transparent shapes of membership functions during the optimization process of the clustering algorithm. To avoid local optimality, the genetic algorithm is applied to solve the optimization problem in the clustering algorithm. Numeric experiments based on data collected in a real civil project demonstrate the feasibility and superiority of the proposed new algorithm. The classification problems solved by fuzzy decision trees based on different linguistic interfaces are provided to demonstrate the advantages of the proposed linguistic interface generation method.

The fuzzy c-means algorithm (FCM) is widely used in the generation of membership functions from historical data. However, most of FCM-based membership function generation algorithms consider little on the transparency or the understandability of the resulting membership functions. In other words, there is inconsistency in generating membership functions using traditional FCM algorithm. This paper proposes a pre-shaped fuzzy c-means algorithm (PFCM) to generate more transparent membership functions. PFCM will preserve the predefined transparent shapes of membership functions during the process of the optimization of the clustering algorithm. Numeric experiments based on data collected in a real project demonstrate the feasibility and superiority of the proposed new algorithm.

Building transparent and highly interpretable models of the construction performance is generally of significant importance to construction managers. However, previous research focuses more on the approximation accuracy of construction performance models. Few studies have been done on the transparency of models, i.e., offering some understandable cause-effect relationships between the construction performance indicator and its influence factors. In this paper, a transparent construction performance model is proposed. First, a neural network, named General Regression Neural Network (GRNN) is selected as the basic modeling technique. Its new genetic algorithm based learning algorithm is introduced. The GRNN not only presents a high approximation rate, but also offers importance indices about the influence of inputs on the output. Secondly, a fuzzy clustering algorithm is introduced to granulate the inputs into their linguistic terms. The model built with the use of granulated data provides clearer influence factors and the indicator of resulting construction performance. The proposed method is tested on the data collected from construction sites. The results demonstrate the feasibility and efficiency of the proposed model