Semi-supervised multiview clustering has garnered considerable attention for its ability to integrate multiview data with limited labeled information. However, existing methods predominantly focus on labeled samples, neglecting abundant unlabeled data, which leads to suboptimal utilization of available prior knowledge. Moreover, existing pairwise constraint propagation-based methods typically follow a two-stage procedure, resulting in unstable clustering outcomes. To address these limitations, we introduce a unified framework that integrates multiview subspace clustering with pairwise constraint propagation, proposing a tensor-based semi-supervised multiview subspace clustering (TSMSC) method with pairwise constraint propagation. Specifically, each view's subspace representation is decomposed into a consensus part and private parts, enabling the consensus representation to better approximate the low-rank structure. Then, a pairwise constraint propagation method is developed for multiview data, which propagates the initial pairwise constraint through a low-rank matrix completion approach. Finally, observing that the ideal multiview consensus representation and the propagated pairwise constraint matrix share the same low-rank structure, we naturally construct them into a third-order tensor to capture high-order correlations via tensor low-rank representation, allowing for joint optimization within a unified framework. Extensive experiments on eight real-world datasets demonstrate the superiority of our method over state-of-the-art approaches.

The emergence of large language models (LLMs) has been a key enabler of technological innovation in healthcare. People can conveniently obtain a more accurate medical consultation service by utilizing LLMs' powerful knowledge inference capability. However, existing LLMs require users to upload explicit requests during remote healthcare consultations, which involves the risk of exposing personal privacy. Furthermore, the reliability of the response content generated by LLMs is not guaranteed. To tackle the above challenges, this paper proposes a novel privacy-preserving LLM for user-activated health, called Adaptive Compressed-based Privacy-preserving LLM (ACP2LLM). Specifically, an adaptive token compression method based on information entropy is carefully designed to ensure that ACP2LLM can preserve user-sensitive information when invoking the medical consultation of LLMs deployed on the cloud platform. Moreover, a multi-doctor one-chief physician mechanism is proposed to rationally split and collaboratively infer the patients' requests to achieve the privacy-utility trade-off. Notably, the proposed ACP2LLM also provides highly competitive performance in various token compression rates. Extensive experiments on multiple Medical Question and Answers datasets demonstrate that the proposed ACP2LLM has strong privacy protection capabilities and high answer precision, outperforming current state-of-the-art LLM methods.

Active learning can significantly reduce the cost of labeling instances while improving model performance. However, similar to other traditional algorithms, active learning encounters the problem of class imbalance and delivers sub-optimal performance. Additionally, existing approaches suffer from poor performance and are time-consuming. To address these issues, we propose an Actively Incrementally Weighted Broad Learning System (AI-WBLS). Firstly, we introduce an active learning framework based on the weighted broad learning system, which employs a double uncertainty sample selection strategy to enhance the value and reasonableness of sample selection in each iteration of active learning. To further improve the model's adaptability during the iterative learning process, an adaptive weighting strategy is designed to adaptively modify the penalty weights according to the changes in the sample labels. Finally, an efficient incremental paradigm is developed to update the model with newly labelled samples instead of re-training, resulting in improved performance and efficiency. Extensive comparative experiments confirm that our approach outperforms other imbalanced active learning methods.

In this paper, a novel deep learning-based no-reference video quality assessment (NR-VQA) model for screen content videos (SCVs) is proposed, called the hierarchical spatiotemporal perceptual quality model (HSPQ). Firstly, the human visual system (HVS) perceives SCVs hierarchically, with varying sensitivity and attention to diverse attribute regions. Secondly, the visual redundancies are copious in the spatiotemporal domain of SCVs, degrading video quality to some extent. Based on these characteristics, the SCVs are decomposed into three hierarchical levels (i.e., patch level, frame level, and video level), which contain quality-related spatiotemporal information. Specifically, the visual saliency is first utilized for more salient textual and pictorial patches selection, and then, a dual-channel convolutional neural network integrating spatial-gate feature enhancement module (SGFEM) is designed to evaluate the quality of patches based on their attributes at the patch level separately. With spatial correlation, an adaptive blur-focused visual mechanism-based weighting strategy (BFWS) is proposed for converting quality scores from patch level to frame level. Finally, the video-level quality score, which reflects the temporal perceptual quality degradation, is combined to provide a comprehensive evaluation of distorted SCV quality. Experiments conducted on the Screen Content Video Database (SCVD) and Compressed Screen Content Video Quality (CSCVQ) databases demonstrate that our proposed HSPQ model aligns better with the visual perception of SCVs by the HVS. Moreover, it exhibits strong robustness compared to multiple classic and state-of-the-art image/video quality assessment models.

In the swiftly advancing realm of information retrieval, unsupervised cross-modal hashing has emerged as a focal point of research, taking advantage of the inherent advantages of the multifaceted and dynamism inherent in multimedia data. Existing unsupervised cross-modal hashing methods rely mainly on initial pre-trained correlations among cross-modal features, and the inaccurate neighborhood correlations impacts the presentation of common semantics throughout the optimization. To address the aforementioned issues, we proposeEnsemblePrototypeNetworks (EPNet), which delineates class attributes of cross-modal instances through an ensemble clustering methodology. EPNet seeks to extract correlation information between instances by leveraging local correlation aggregation and ensemble clustering from multiple perspectives, aiming to reduce initialization effects and enhance cross-modal representations. Specifically, the local correlation aggregation is first proposed within a batch of semantic affinity relationships to generate a precise and compact hash code among cross-modal instances. Secondly, the ensemble prototype module is employed to discern the class attributes of deep features, thereby aiding the model in extracting more universally applicable feature representations. Thirdly, an early attempt to constrict the representational congruity of local semantic affinity relationships and deep feature ensemble prototype correlations using cross-task consistency loss aims to enhance the representation of cross-modal common semantic features. Finally, EPNet outperforms several state-of-the-art cross-modal retrieval methods on three real-world image-text datasets in extensive experiments.

In the current era of big data, securing computer networks and preventing cyberattacks have become a major challenge. In this regard, we present a novel approach for addressing the imbalanced problem of network traffic data. Specifically, the proposed method MC-OCBLS, leverages the maximum correntropy criterion (MCC) to develop a one-class broad learning system. The robustness of the one-class classification model is enhanced by maximizing the correlation entropy. Moreover, considering the limited generalization capability of individual models, we propose the boosting ensemble model SMC-OCBLS, which assigns higher weights to misclassified samples and classifiers with lower error rates. To increase the variety of classifiers in the ensemble framework, feature columns are randomly disrupted during training to construct different feature spaces. Training multiple hybrid classifiers using various feature spaces further enhances the accuracy and generalizability of the model. The experimental results demonstrate the superiority of the proposed model over other advanced approaches.

Consensus clustering can derive a more promising and robust clustering result by integrating multiple partitions strategically. However, there are several limitations in the existing approaches: 1) most of the methods compute the ensemble-information matrix heuristically and lack of sufficient optimization; 2) the information from the original dataset is rarely considered; and 3) the noise in both label space and feature space is ignored. To address these issues, we proposed a novel consensus clustering method with co-association matrix optimization (CC-CMO), which aims at improving the co-association matrix by taking abundant information from both label space and feature space into consideration. In label space, CC-CMO derives a weighted partition matrix capturing the intercluster correlation and further designs a least squares regression (LSR) model to explore the global structure of data. In feature space, CC-CMO minimizes the reconstruction error with doubly stochastic normalization in the projective subspace to eliminate noise features as well as learn the local affinity of data. To improve the co-association matrix by jointly considering the subspace representation, global structure, and local affinity of data, we explicitly propose a unified optimization framework and design an alternating optimization algorithm for the optimal co-association matrix. Extensive experiments on a variety of real-world datasets demonstrate the superior performance of CC-CMO to the state-of-the-art consensus clustering approaches.

Consensus clustering methods provide better performance by fusing multiple clustering solutions in terms of accuracy, robustness and stability. However, most current methods suffer from different challenges: i) the high-dimensional problem; ii) limitations of single clustering method; iii) the optimal number of clusters selecting for a certain validity measure; iv) redundant clustering candidate attributes. To overcome the above limitations, we propose a hybrid clustering solutions fusion method based on gated three-way decision (HCFG) for data analysis. By integrating multiple clustering solutions and executing information fusion, HCFG enjoys four properties: (1) multiple random subspace generation strategy is utilized to generate diverse low-dimensional subspaces effectively; (2) a fusion framework that considers characteristics of both the soft clustering and hard clustering methods is designed, in which potential boundary of feature attribute sets is explored; (3) the optimal number of clusters is set by utilizing multiple clustering validity indices; (4) clustering solutions is considered as attributes and a gated three-way decision method is proposed to adaptively conduct attribute reductions. Extensive comparative experiments on 24 real-world data sets demonstrates the effectiveness and superiority of HCFG. Moreover, nonparametric tests are conducted to compare HCFG with multiple consensus clustering methods.

With the vigorous development of Industry 4.0, industrial Big Data has turned into the core element of the Industrial Internet of Things. As one of the most fundamental and indispensable components in industrial cyber-physical systems (CPS), intelligent anomaly detection is still an essential and challenging issue. However, with the development of the network, there may exist unknown types of attacks, which are difficult to collect. Facing one-class industrial intrusion detection scenario that the collected training data only includes normal state, the one-class broad learning system (OCBLS) and the stacked OCBLS (ST-OCBLS) algorithms are developed. Benefiting from the characteristics of BLS, our proposed approaches retain the advantage of efficient training process. Moreover, the high-level hidden features of the network traffic data can be learned through the progressive encoding and decoding mechanism in ST-OCBLS. Extensive comparative experiments on several real-world intrusion detection tasks are carried out to demonstrate that our proposed methods have competitive performance and high efficiency in the face of complex network data and diversified types of intrusions. Overall, this article provides a new alternative solution for network intrusion detection in Industry 4.0.

Ensemble clustering has an advantage in producing a more promising and robust clustering result by combining multiple partitions strategically. The quality of both base partitions and co-association matrix plays an essential role in improving the consensus partition. However, the current ensemble clustering methods have several limitations: 1) The noise in high-dimensional feature space is ignored; 2) The independent base partition generation process does not pay attention to ambiguous samples; 3) The co-association matrix and the weights of base partitions commonly lack of theoretical optimization. In order to address these issues, we propose an adaptive ensemble clustering framework with boosting BLS-based autoencoder (BoostAEC). In the generation step, a boosting BLS-based autoencoder (BoostBLSAE) is designed to generate base partitions sequentially, which learns compressed feature subspaces for ambiguous samples and adaptively evaluates the corresponding weights of reliability. In the integration step, we construct a fuzzy membership function to capture the inter-cluster correlation and explicitly propose a consensus objective function to optimize the unified co-association matrix by considering the weighted base partitions. Extensive experiments on various real-world datasets demonstrate the superior performance of BoostAEC to the state-of-the-art ensemble clustering methods.

The amount of multi-modal data available on the Internet is enormous. Cross-modal hash retrieval maps heterogeneous cross-modal data into a single Hamming space to offer fast and flexible retrieval services. However, existing cross-modal methods mainly rely on the feature-level similarity between multi-modal data and ignore the relationship between relative rankings and label-level fine-grained similarity of neighboring instances. To overcome these issues, we propose a novelDeepCross-modalHashing based onSemanticConsistentRanking (DCH-SCR) that comprehensively investigates the intra-modal semantic similarity relationship. Firstly, to the best of our knowledge, it is an early attempt to preserve semantic similarity for cross-modal hashing retrieval by combining label-level and feature-level information. Secondly, the inherent gap between modalities is narrowed by developing a ranking alignment loss function. Thirdly, the compact and efficient hash codes are optimized based on the common semantic space. Finally, we use the gradient to specify the optimization direction and introduce the Normalized Discounted Cumulative Gain (NDCG) to achieve varying optimization strengths for data pairs with different similarities. Extensive experiments on three real-world image-text retrieval datasets demonstrate the superiority of DCH-SCR over several state-of-the-art cross-modal retrieval methods.

Cross-modal hashing retrieval approaches maps heterogeneous multi-modal data into a common hamming space to achieve efficient and flexible retrieval performance. However, existing cross-modal methods mainly exploit feature-level similarity between multi-modal data, the label-level similarity and relative ranking relationship between adjacent instances have been ignored. To address these problems, we propose a novel Deep Rank Cross-modal Hashing(DRCH) method that fully explores the intra-modal semantic similarity relationship. Firstly, DRCH preserves semantic similarity by combining both label-level and feature-level information. Secondly, the inherent gap between modalities are narrowed by proposing a ranking alignment loss function. Finally, the compact and efficient hash codes are optimized from the common semantic space. Extensive experiments on two real-world image-text retrieval datasets demonstrate the superiority of DRCH compared with several state-of-the-art(SOTA) methods.

Learning and classification of imbalanced data is a quite common challenge in machine learning. In order to generate more high-quality minority samples in imbalanced data and help solve the classification process, this paper proposes a novel local tangent generative adversarial network (LT-GAN). In LT-GAN, a local tangent based generator is designed to generate realistic and diverse minority class samples by learning the local tangent space of original minority samples. Meanwhile, a two-function discriminator is explored to judge the authenticity of samples and distinguish the majority samples from the minority samples. With the synthesis of minority class samples, the generator and discriminator are trained together by using adversarial learning. Experiments and comparisons show that our proposed LT-GAN outperforms other techniques and significantly improves the classification performance of imbalanced data.

The newly proposed Broad Learning System (BLS) offers an alternative way to deep learning which saves a time-consuming training process and powerful computing resources. However, the randomly generated feature nodes and lots of enhancement nodes in BLS may reduce the performance of the final classifier. Aiming at the problems in randomly generated feature nodes which suffer from unpredictability and need guidance, this paper proposes an adaptive feature nodes evolutionary algorithm (AFNE) to extract better features; While broad learning neural network often requires a large number of enhancement nodes parameters to ensure its performance, which easily leads to the redundancy or dependency between features, as well as the performance degradation of the final model. Therefore, this article also proposes a new criterion based on node sensitivity to prune the network enhancement layer nodes to remove redundant nodes, reduce the network scale, and increase the generalization ability. The proposed algorithm ADP-BLS can improve the accuracy and generalization performance of the final classifier through the evolution of feature nodes and the pruning of enhancement nodes. Extensive comparative experiments on real world data sets verify the effectiveness of the proposed ADP-BLS. At the same time, when verifying the module validity of the innovative algorithms added in this article, experiments also show that the AFNE and pruning method integrated into BLS can improve the model to a certain extent.

Semisupervised clustering methods improve performance by randomly selecting pairwise constraints, which may lead to redundancy and instability. In this context, active clustering is proposed to maximize the efficacy of annotations by effectively using pairwise constraints. However, existing methods lack an overall consideration of the querying criteria and repeatedly run semisupervised clustering to update labels. In this work, we first propose an active density peak (ADP) clustering algorithm that considers both representativeness and informativeness. Representative instances are selected to capture data patterns, while informative instances are queried to reduce the uncertainty of clustering results. Meanwhile, we design a fast-update-strategy to update labels efficiently. In addition, we propose an active clustering ensemble framework that combines local and global uncertainties to query the most ambiguous instances for better separation between the clusters. A weighted voting consensus method is introduced for better integration of clustering results. We conducted experiments by comparing our methods with state-of-the-art methods on real-world data sets. Experimental results demonstrate the effectiveness of our methods.

Clustering ensemble (CE) takes multiple clustering solutions into consideration in order to effectively improve the accuracy and robustness of the final result. To reduce redundancy as well as noise, a CE selection (CES) step is added to further enhance performance. Quality and diversity are two important metrics of CES. However, most of the CES strategies adopt heuristic selection methods or a threshold parameter setting to achieve tradeoff between quality and diversity. In this paper, we propose a transfer CES (TCES) algorithm which makes use of the relationship between quality and diversity in a source dataset, and transfers it into a target dataset based on three objective functions. Furthermore, a multiobjective self-evolutionary process is designed to optimize these three objective functions. Finally, we construct a transfer CE framework (TCE-TCES) based on TCES to obtain better clustering results. The experimental results on 12 transfer clustering tasks obtained from the 20newsgroups dataset show that TCE-TCES can find a better tradeoff between quality and diversity, as well as obtaining more desirable clustering results.