Imbalanced class distribution disrupts the training of a classifier, resulting in biases favoring majority classes. Data oversampling is a common strategy to tackle this issue. However, traditional methods may generate incorrect and unnecessary instances when facing complex data challenges, such as class overlap, small disjuncts, and noise samples. Therefore, there is a need for an oversampling method that can accurately characterize the data distribution. This paper introduces a novel deep generative oversampling approach for balancing the imbalanced tabular data by leveraging diffusion models and Generative Adversarial Networks (GANs). The model comprises a generator constructed from diffusion models and a discriminator with a Noise-Sensitive Auxiliary Classifier (NSAC) and is trained through an adversarial process. The synergy of these two models enhances stability and sample quality compared to GANs, with faster sampling speed and better conditional generating ability than diffusion models. In experimental validation across 22 real-world datasets, our method consistently outperforms six counterparts regarding Accuracy, F1-score, and MCC for binary and multi-class scenarios. Notably, our approach enhances classifier accuracy for minority classes while maintaining a high level for the majority class, a facet often compromised by other algorithms.

There are a large number of text processing tasks in web applications, such as sentiment classification, summary extraction, and question answering. Recently, fine-tuning pre-trained language models (PLMs) to adapt to downstream text-processing tasks has attracted much attention. However, due to the differences in data, model, and tasks between the pre-training and fine-tuning processes, the fine-tuning process may suffer from catastrophic forgetting of pre-training knowledge, which may implicitly limit the model's performance and generalization ability. To address these challenges, we propose a novel dual-model framework, termed as consistency alignment (CoAi). The insight of CoAi lies in building an auxiliary model that simulates the distribution of pre-training knowledge in real-time according to the current task, and co-training the task-specific model and the auxiliary model to balance the pre-training knowledge and task-specific knowledge during fine-tuning. Specifically, the auxiliary model is constructed on-the-fly to maintain the pre-training knowledge. Subsequently, CoAi simulates the pre-training process by performing distributional exploration in the parameter space, which is built upon our novel insight into the transformation between data and model parameter space. However, the objectives leveraged to construct the auxiliary model lead to the misalignment between the pre-training and task-specific knowledge. To alleviate the inconsistency, we employ an auxiliary variable to align the prediction distribution of the task-specific and the auxiliary models, inspired by constrastive clustering. We validate the effectiveness of CoAi on nine classic classification tasks and three generation tasks, showing consistent and significant improvements compared with state-of-the-art methods.

Clustering is a popular machine learning technique for data mining that can process and analyze datasets to automatically reveal sample distribution patterns. Since the ubiquitous categorical data naturally lack a well-defined metric space such as the Euclidean distance space of numerical data, the distribution of categorical data is usually under-represented, and thus valuable information can be easily twisted in clustering. This paper, therefore, introduces a novel order distance metric learning approach to intuitively represent categorical attribute values by learning their optimal order relationship and quantifying their distance in a line similar to that of the numerical attributes. Since subjectively created qualitative categorical values involve ambiguity and fuzziness, the order distance metric is learned in the context of clustering. Accordingly, a new joint learning paradigm is developed to alternatively perform clustering and order distance metric learning with low time complexity and a guarantee of convergence. Due to the clustering-friendly order learning mechanism and the homogeneous ordinal nature of the order distance and Euclidean distance, the proposed method achieves superior clustering accuracy on categorical and mixed datasets. More importantly, the learned order distance metric greatly reduces the difficulty of understanding and managing the non-intuitive categorical data. Experiments with ablation studies, significance tests, case studies, etc., have validated the efficacy of the proposed method. The source code is available at https://github.com/csmjzhao/OCL_Source_Code.

Although Graph Neural Networks (GNNs) have exhibited the powerful ability to gather graph-structured information from neighborhood nodes via various message-passing mechanisms, the performance of GNNs is limited by poor generalization and fragile robustness caused by noisy and redundant graph data. As a prominent solution, Graph Augmentation Learning (GAL) has recently received increasing attention in the literature. Among the existing GAL approaches, edge-dropping methods that randomly remove edges from a graph during training are effective techniques to improve the robustness of GNNs. However, randomly dropping edges often results in bypassing critical edges. Consequently, the effectiveness of message passing is weakened. In this paper, we propose a novel adversarial edge-dropping method (ADEdgeDrop) that leverages an adversarial edge predictor guiding the removal of edges, which can be flexibly incorporated into diverse GNN backbones. Employing an adversarial training framework, the edge predictor utilizes the line graph transformed from the original graph to estimate the edges to be dropped, which improves the interpretability of the edge-dropping method. The proposed ADEdgeDrop is optimized alternately by stochastic gradient descent and projected gradient descent. Comprehensive experiments on eight graph benchmark datasets demonstrate that the proposed ADEdgeDrop outperforms state-of-the-art baselines across various GNN backbones, demonstrating improved generalization and robustness.

Class imbalance, which is common in real-world classification tasks, often leads to biased models favoring majority classes. Data oversampling is a widely used strategy to address this issue. However, traditional oversampling methods often generate incorrect or redundant instances when class overlap occurs, increasing decision boundary complexity. To this end, we propose a novel Generative Oversampling approach to addressing Class Imbalance and Overlap (GOIO) in the classification of tabular data. GOIO combines a Metric-Learning-based Variational Autoencoder (MLVAE) and a Conditional Latent Diffusion Model (CLDM) to handle class imbalance and overlap effectively. The MLVAE employs a triplet-center loss to the adverse effects of class overlap by transforming the data distribution into a more separable latent feature space. Following this, the CLDM is trained with class-center feature prompting and classifier-free guidance strategy to capture class-specific latent distributions accurately. Minority class samples are synthesized in the latent space using the CLDM and then reconstructed into the data space via the MLVAE decoder. Comprehensive experiments on 18 real-world and five synthetic datasets demonstrate that GOIO outperforms the state-of-the-art oversampling methods in F1-score, MCC, and Accuracy. Ablation studies further validate the effectiveness of the proposed contributions in addressing class imbalance and overlap.

Clustering is one of the most commonly used techniques for unsupervised data analysis.As real data sets are usually composed of numerical and categorical features that are heterogeneous in nature, the heterogeneity in the distance metric and feature coupling prevents deep representation learning from achieving satisfactory clustering accuracy.Currently, supervised Quaternion Representation Learning (QRL) has achieved remarkable success in efficiently learning informative representations of coupled features from multiple views derived endogenously from the original data.To inherit the advantages of QRL for unsupervised heterogeneous feature representation learning, we propose a deep QRL model that works in an encoder-decoder manner.To ensure that the implicit couplings of heterogeneous feature data can be well characterized by representation learning, a hierarchical coupling encoding strategy is designed to convert the data set into an attributed graph to be the input of QRL.We also integrate the clustering objective into the model training to facilitate a joint optimization of the representation and clustering.Extensive experimental evaluations illustrate the superiority of the proposed Quaternion Graph Representation Learning (QGRL) method in terms of clustering accuracy and robustness to various data sets composed of arbitrary combinations of numerical and categorical features.The source code is opened at https://github.com/Juny-Chen/QGRL.git.

Oracle Bone Inscriptions (OBIs) represent the early pictographic writing system of the matured Chinese civilization, documenting the history of the Shang dynasty. Deciphering them holds significant importance for unraveling the origins of civilization. Recently, an increasing number of algorithms have been proposed to assist in deciphering OBIs. However, most of these efforts have focused on the character level, thus offering limited assistance. Considering the presence of many similar components within OBI characters, associating different OBI characters with the same component will facilitate OBI decipherment. In this paper, we therefore propose a component-level OBI retrieval task, i.e., using an OBI component to retrieve all OBI characters containing this component. We accordingly collect a dataset, termed OBI component 20, containing 10,257 OBIs, which is annotated by OBI experts. Then, we propose a dual-stream attention-based model and two types of triplets based on components and characters as anchors to model the relationships between components and characters. Specifically, these two types of triplets ensure that characters containing different components are further apart, while those containing the same component are closer to the corresponding component. Experimental results demonstrate the effectiveness of our proposed model.

Federated Learning (FL) learns a global model in a distributional manner, which does not require local clients to share private data. Such merit has drawn lots of attention in the interaction scenarios, where Federated Reinforcement Learning (FRL) emerges as a cross-field research direction focusing on the robust training of agents. Different from FL, the heterogeneity problem in FRL is more challenging because the data depends on the policy of agents and the environment dynamics. FRL learns to interact under the non-stationary environment feedback, while the typical FL methods aim at handling the constant data heterogeneity. In this article, we are among the first attempts to analyze the heterogeneity problem in FRL and propose an off-policy FRL framework. Specifically, a student–teacher–student model learning and fusion method, termed asServer-Client Collaborative Distillation(SCCD), is introduced. Unlike the traditional FL, we distill all local models on the server side for model fusion. To reduce the variance of the training, a local distillation is also conducted every time the agent receives the global model. Experimentally, we compare SCCD with a range of straightforward combinations between FL methods and RL. The results demonstrate that SCCD has a superior performance in four classical continuous control tasks with non-IID environments.

AdaBoost approaches have been used for multi-class imbalance classification with an imbalance ratio measured on class sizes. However, such ratio would assign each training sample of the same class with the same weight, thus failing to reflect the data distribution within a class. We propose to incorporate the density information of training samples into the class imbalance ratio so that samples of the same class could have different weights. As one could use the entire training set to calculate the imbalance and density factors, the weight of a training sample resulting from the two factors remains static throughout the training epochs. However, static weights could not reflect the up-to-date training status of base learners. To deal with this, we propose to design an adaptive weighting mechanism by making use of up-to-date training status to further alleviate the multi-class imbalance issue. Ultimately, we incorporate the class imbalance ratio, the density-based factor, and the adaptive weighting mechanism into a single variable, based on which the adaptive weights of all training samples are computed. Experimental studies are carried out to investigate the effectiveness of the proposed approach and each of the three components in dealing with multi-class imbalance classification problem.

It is a key issue to find out all longest common subsequences of multiple sequences over a set of finite alphabets, namely MLCS problem, in computational biology, pattern recognition and information retrieval, to name a few. However, it is very challenging to tackle the large-scale MLCS problem effectively and efficiently due to the high complexity of time and space. To this end, this paper will therefore propose a Branch Elimination based Space and Time efficient algorithm called BEST-MLCS, which includes the following four key strategies: 1) Estimation scheme for the lower bound of the length of MLCS. 2) Estimation scheme for the upper bound of the length of the paths through the current node. 3) Branch elimination strategy by finding all useless match points and removing the branches not on the longest paths. 4) A new Directed Acyclic Graph (DAG) construction method for constructing the smallest DAG among the existing ones. As a result, the proposed algorithm BEST-MLCS can save a lot of space and time and can handle much larger scale MLCS problems than the existing algorithms. Extensive experiments conducted on biological DNA sequences show that the performance of the proposed algorithm BEST-MLCS outperforms three state-of-the-art algorithms in terms of run-time and memory consumption.

This paper focuses on an emerging challenging problem called heterogeneous prototype learning (HPL) across face domains-It aims to learn the variation-free target domain prototype for a contaminated input image from the source domain and meanwhile preserve the personal identity. HPL involves two coupled subproblems, i.e., domain transfer and prototype learning. To address the two subproblems in a unified manner, we advocate disentangling the prototype and domain factors in their respected latent feature spaces, and replace the latent source domain features with the target domain ones to generate the heterogeneous prototype. To this end, we propose a disentangled heterogeneous prototype learning framework, dubbed DisHPL, which consists of one encoder-decoder generator and two discriminators. The generator and discriminators play adversarial games such that the generator learns to embed the contaminated image into a prototype feature space only capturing identity information and a domain-specific feature space, as well as generating a realistic-looking heterogeneous prototype. The two discriminators aim to predict personal identities and distinguish between real prototypes versus fake generated prototypes in the source/target domain. Experiments on various heterogeneous face datasets validate the effectiveness of DisHPL.

Searching Longest Common Subsequences (LCS) of Multi-ple (i.e., three or more) sequences, denoted as MLCS for short, is a fundamental problem in computational biology, pattern recognition, file comparison and information retrieval, to name a few. Basically, an MLCS problem is more challenging than the LCS of two sequences which can be solved by a dynamic programming (DP) method whose running time and memory space are both$O(n^{2})$, where$n$is the length of the sequences.

Many researches in cognitive science have shown that humans often perform face-voice association for various perception tasks, and some recent data mining works have been designed in emulating such ability intelligently. Nevertheless, most methods often suffer from the degraded performance when there exist semantically irrelevant interference factors across different modalities. To alleviate this concern, this paper presents an efficient Disentangled Cross-modal Latent Representation (DCLR) method to adaptively detach the discriminative feature attributes and enhance the face-voice association. To be specific, the proposed DCLR framework consists of two-stage cross-modal disentangling process. First, the former stage employs the supervised contrastive learning to push the representations of face-voice data from the same person closer while pulling those representations of different person away. Then, the latter stage freezes all the parameters of the former stage, and further innovates a multi-layer orthogonal decoupling scheme to learn the disentangled latent representations, while filtering out the modality-dependent irrelevant factors. Besides, the cross-modal reconstruction loss is further utilized to narrow down the semantic gap between heterogeneous feature expressions. Through the joint exploitation of the above, the proposed framework can well associate the face-voice data to benefit various kinds of cross-modal perception tasks. Extensive experiments verify the superiorities of the proposed face-voice association framework and show its competitive performances.

In some practical scenarios, such as video surveillance and personal identification, we often have to address the recognition problem of occluded faces, where content replacement by serious occlusion with non-face objects always produces partial appearance and ambiguous representation. Under the circumstances, the performance of face recognition algorithms will often deteriorate to a certain degree. In this paper, we therefore address this problem by removing occlusions on face images and present a new two-stage Facial Structure Guided Generative Adversarial Network (FSG-GAN). In Stage I of the FSG-GAN, the variational auto-encoder is used to predict the facial structure. In Stage II, the predicted facial structure and the occluded image are concatenated and fed into a generative adversarial network (GAN) based model to synthesize the de-occlusion face image. In this way, the facial structure knowledge can be transferred to the synthesis network. Especially, in order to enable the occluded face image to be perceived well, the generator in the GAN based synthesis network utilizes the hybrid dilated convolution modules to extend the receptive field. Furthermore, aiming at further eliminating the appearance ambiguity as well as unnatural texture, a multi-receptive fields discriminator is proposed to utilize the features from different levels. Experiments on the benchmark datasets show the efficacy of the proposed FSG-GAN.

Fine-grained Image-text retrieval is challenging but vital technology in the field of multimedia analysis. Existing methods mainly focus on learning the common embedding space of images (or patches) and sentences (or words), whereby their mapping features in such embedding space can be directly measured. Nevertheless, most existing image-text retrieval works rarely consider the shared semantic concepts that potentially correlated the heterogeneous modalities, which can enhance the discriminative power of learning such embedding space. Toward this end, we propose a Cross-Graph Attention model (CGAM) to explicitly learn the shared semantic concepts, which can be well utilized to guide the feature learning process of each modality and promote the common embedding learning. More specifically, we build semantic-embedded graph for each modality, and smooth the discrepancy between two modalities via cross-graph attention model to obtain shared semantic-enhanced features. Meanwhile, we reconstruct image and text features via the shared semantic concepts and original embedding representations, and leverage multi-head mechanism for similarity calculation. Accordingly, the semantic-enhanced cross-modal embedding between image and text is discriminatively obtained to benefit the fine-grained retrieval with high retrieval performance. Extensive experiments evaluated on benchmark datasets show the performance improvements in comparison with state-of-the-arts.

Many cognitive researches have shown the natural possibility of face-voice association, and such potential association has attracted much attention in biometric cross-modal retrieval domain. Nevertheless, the existing methods often fail to explicitly learn the common embeddings for challenging face-voice association tasks. In this paper, we present to learn discriminative joint embedding for face-voice association, which can seamlessly train the face subnetwork and voice subnetwork to learn their high-level semantic features, while correlating them to be compared directly and efficiently. Within the proposed approach, we introduce bi-directional ranking constraint, identity constraint and center constraint to learn the joint face-voice embedding, and adopt bi-directional training strategy to train the deep correlated face-voice model. Meanwhile, an online hard negative mining technique is utilized to discriminatively construct hard triplets in a mini-batch manner, featuring on speeding up the learning process. Accordingly, the proposed approach is adaptive to benefit various face-voice association tasks, including cross-modal verification, 1:2 matching, 1:N matching, and retrieval scenarios. Extensive experiments have shown its improved performances in comparison with the state-of-the-art ones.

Most Cross-modal hashing methods do not sufficiently exploit the discrimination power of semantic information when learning hash codes, while often involving time-consuming training procedures for large-scale dataset. To tackle these issues, we first formulate the learning of similarity-preserving hash codes in terms of orthogonally rotating the semantic data to hamming space, and then propose a novel Fast Semantic Preserving Hashing (FSePH) approach to large-scale cross-modal retrieval. Specifically, FSePH introduces an orthonormal basis to regress the targeted hash codes of training examples to their corresponding reasonably relaxed class labels, featuring significantly reducing the quantization error. Meanwhile, an effective optimization algorithm is derived for modality-specific projection function learning and an efficient closed-form solution for hash code learning, which are computationally tractable. Extensive experiments have shown that the proposed FSePH approach runs sufficiently fast, and also significantly improves the retrieval performances over the state-of-the-arts.

With the dramatic increase of multi-media data on the Internet, cross-modal retrieval has become an important and valuable task in searching systems. The key challenge of this task is how to build the correlation between multi-modal data. Most existing approaches only focus on dealing with paired data. They use pairwise relationship of multi-modal data for exploring the correlation between them. However, in practice, unpaired data are more common on the Internet but few methods pay attention to them. To utilize both paired and unpaired data, we propose a one-stream framework triplet fusion network hashing (TFNH), which mainly consists of two parts. The first part is a triplet network which is used to handle both kinds of data, with the help of zero padding operation. The second part consists of two data classifiers, which are used to bridge the gap between paired and unpaired data. In addition, we embed manifold learning into the framework for preserving both inter and intra modal similarity, exploring the relationship between unpaired and paired data and bridging the gap between them in learning process. Extensive experiments show that the proposed approach outperforms several state-of-the-art methods on two datasets in paired scenario. We further evaluate its ability of handling unpaired scenario and robustness in regard to pairwise constraint. The results show that even we discard 50% data under the setting in [19], the performance of TFNH is still better than that of other unpaired approaches and that only 70% pairwise relationships are preserved, TFNH can still outperform almost all paired approaches.

Single sample face recognition is one of the most challenging problems in face recognition (FR), where only one single sample per person (SSPP) is enrolled in the gallery set for training. Although patch-based methods have achieved great success in FR with SSPP, they still have significant limitations. In this work, we propose a new patch-based method, namely Robust Heterogeneous Discriminative Analysis (RHDA), to tackle FR with SSPP. Compared with the existing patch-based methods, RHDA can enhance the robustness against complex facial variations from two aspects. First, we develop a novel Fisher-like criterion, which incorporates two manifold embeddings, to learn heterogeneous discriminative representations of image patches. Specifically, for each patch, the Fisher-like criterion is able to preserve the reconstruction relationship of neighboring patches from the same person, while suppressing neighboring patches from different persons. Second, we present two distance metrics, i.e., patch-to-patch distance and patch-to-manifold distance, and develop a fusion strategy to combine the recognition outputs of above two distance metrics via joint majority voting for identification. Experimental results on the AR and FERET benchmark datasets demonstrate the efficacy of the proposed method.

Tensor completion (TC) is a challenging problem of recovering missing entries of a tensor from its partial observation. One main TC approach is based on CP/Tucker decomposition. However, this approach often requires the determination of a tensor rank a priori. This rank estimation problem is difficult in practice. Several Bayesian solutions have been proposed but they often under/over-estimate the tensor rank while being quite slow. To address this problem of rank estimation with missing entries, we view the weight vector of the orthogonal CP decomposition of a tensor to be analogous to the vector of singular values of a matrix. Subsequently, we define a new CP-based tensor nuclear norm as the $L_1$-norm of this weight vector. We then propose Tensor Rank Estimation based on $L_1$-regularized orthogonal CP decomposition (TREL1) for both CP-rank and Tucker-rank. Specifically, we incorporate a regularization with CP-based tensor nuclear norm when minimizing the reconstruction error in TC to automatically determine the rank of an incomplete tensor. Experimental results on both synthetic and real data show that: 1) Given sufficient observed entries, TREL1 can estimate the true rank (both CP-rank and Tucker-rank) of incomplete tensors well; 2) The rank estimated by TREL1 can consistently improve recovery accuracy of decomposition-based TC methods; 3) TREL1 is not sensitive to its parameters in general and more efficient than existing rank estimation methods.

Media interestingness prediction plays an important role in many real-world applications and attracts much research attention recently. In this paper, we aim to investigate this problem from the perspective of supervised feature extraction. Specifically, we design a novel algorithm dubbed Multi-view Manifold Learning (M) to uncover the latent factors that are capable of distinguishing interesting media data from non-interesting ones. By modelling both geometry preserving criterion and discrimination maximization criterion in a unified framework, M2L learns a common subspace for data from multiple views. The analytical solution of M2L is obtained by solving a generalized eigen-decomposition problem. Experiments on the Predicting Media Interestingness Dataset validate the effectiveness of the proposed method.

As a fundamental tool in the fields of data mining and computer vision, robust low rank subspace learning is to recover a low rank matrix under gross corruptions that are often modeled by another sparse matrix. Within this learning, we investigate the spectral k-support norm, a more appealing convex relaxation than the popular nuclear norm, as a low rank penalty in this paper. Despite the better recovering performance, the spectral k-support norm entails the model difficult to be optimized efficiently, which severely limits its scalability from the practical perspective. Therefore, this paper proposes a scalable and efficient algorithm which considers the dual objective of the original problem that can take advantage of the more computational efficient linear oracle of the spectral k-support norm to be evaluated. Further, by studying the sub-gradient of the loss of the dual objective, a line-search strategy is adopted in the algorithm to enable it to adapt to the Holder smoothness. Experiments on various tasks demonstrate the superior prediction performance and computation efficiency of the proposed algorithm.

As an effective technique for dimensionality reduction, feature selection has a broad application in different research areas. In this paper, we present a feature selection method based on a novel feature clustering procedure, which aims at partitioning the features into different clusters such that the features in the same cluster contain similar structural information of the given instances. Subsequently, since the obtained feature subset consists of features from variant clusters, the similarity between selected features will be low. This allows us to reserve the most data structural information with the minimum number of features. Experimental results on different benchmark data sets demonstrate the superiority of the proposed method.

Outlier often degrades the classification and cluster accuracy. In this paper, we present an outlier detection approach based on local kernel regression for instance selection. It evaluates the reconstruction error of instances by their neighbors to identify the outliers. Experiments are performed both on the synthetic and real-life data sets to show the efficacy of the proposed approach in comparison with the existing counterparts.

The pairwise constraints specifying whether a pair of samples should be grouped together or not have been successfully incorporated into the conventional clustering methods such as k-means and spectral clustering for the performance enhancement. Nevertheless, the issue of pairwise constraints has not been well studied in the recently proposed maximum margin clustering (MMC), which extends the maximum margin framework in supervised learning for clustering and often shows a promising performance. This paper therefore proposes a pairwise constrained MMC algorithm. Based on the maximum margin idea in MMC, we propose a set of effective loss functions for discouraging the violation of given pairwise constraints. For the resulting optimization problem, we show that the original nonconvex problem in our approach can be decomposed into a sequence of convex quadratic program problems via constrained concave-convex procedure (CCCP). Subsequently, we present an efficient subgradient projection optimization method to solve each convex problem in the CCCP sequence. Experiments on a number of real-world data sets show that the proposed constrained MMC algorithm is scalable and outperforms the existing constrained MMC approach as well as the typical semi-supervised clustering counterparts.

In general, irrelevant features of high-dimensional data will degrade the performance of an inference system, e.g., a clustering algorithm or a classifier. In this paper, we therefore present a Local Kernel Regression (LKR) scoring approach to evaluate the relevancy of features based on their capabilities of keeping the local configuration in a small patch of data. Accordingly, a score index featuring applicability to both of supervised learning and unsupervised learning is developed to identify the relevant features within the framework of local kernel regression. Experimental results show the efficacy of the proposed approach in comparison with the existing methods.

In this paper, we present an agglomerative fuzzy $k$-means clustering algorithm for numerical data, an extension to the standard fuzzy $k$-means algorithm by introducing a penalty term to the objective function to make the clustering process not sensitive to the initial cluster centers. The new algorithm can produce more consistent clustering results from different sets of initial clusters centers. Combined with cluster validation techniques, the new algorithm can determine the number of clusters in a data set, which is a well known problem in $k$-means clustering. Experimental results on synthetic data sets (2 to 5 dimensions, 500 to 5000 objects and 3 to 7 clusters), the BIRCH two-dimensional data set of 20000 objects and 100 clusters, and the WINE data set of 178 objects, 17 dimensions and 3 clusters from UCI, have demonstrated the effectiveness of the new algorithm in producing consistent clustering results and determining the correct number of clusters in different data sets, some with overlapping inherent clusters.

We propose a market mechanism that can be implemented on clustering aggregation problem among selfish systems, which tend to lie about their correct clustering during aggregation process. Our study is the preliminary step toward the development of robust distributed data mining among selfish systems.

In this paper, a public-key scheme is proposed to authenticate 3D mesh models. It is well known that digital signature schemes can be used for data authentication by generating a separate signature and appending it to the file. Another way is divide the original data into two parts: the content to be authenticated and the cover content. In the embedding process, the signature of the first part is generated and imperceptibly embedded within the cover content to form the signed content. In the authentication process, a new hash value is produced from the signed content and compared with the value decrypted from the retrieved signature for tamper detection. Before we implement such a scheme on polygonal meshes, mesh partitioning technique is used to divide them into patches with a fixed amount of vertices. For each patch, a corresponding signature is generated to replace the least significant bits of vertex coordinates within it so that the tamper can be localized. The experimental results have shown the promising results

The recent Maximum Weighted Likelihood (MWL) [18], [19] has provided a general learning paradigm for density-mixture model selection and learning, in which weight design, however, is a key issue. This paper will therefore explore such a design, and through which a heuristic extended Expectation-Maximization (X-EM) algorithm is presented accordingly. Unlike the EM algorithm [1], the X-EM algorithm is able to perform model selection by fading the redundant components out from a density mixture, meanwhile estimating the model parameters appropriately. The numerical simulations demonstrate the efficacy of our algorithm.

This paper proposes a reversible data hiding method to authenticate 3D meshes by modulating the distances from the mesh faces to the mesh centroid to embed a fragile watermark. It keeps the modulation information in the watermarked mesh so that the reversibility of the embedding process is achieved. Since the embedded watermark is sensitive to geometrical and topological processing, unauthorized modifications on the watermarked mesh can be therefore detected by retrieving and comparing the embedded watermark with the original one. Furthermore, as long as the watermarked mesh is intact, the original mesh can be recovered using some priori knowledge.

Expectation-maximization (EM) algorithm (A.P. Dempster et al., 1977) has been extensively used in density mixture clustering problems, but it is unable to-perform model selection automatically. This paper, therefore, proposes to learn the model parameters via maximizing a weighted likelihood. Under a specific weight design, we give out a rival penalized expectation-maximization (RPEM) algorithm, which makes the components in a density mixture compete each other at each time step. Not only are the associated parameters of the winner updated to adapt to an input, but also all rivals' parameters are penalized with the strength proportional to the corresponding posterior density probabilities. Compared to the EM algorithm (A.P. Dempster et al., 1977), the RPEM is able to fade out the redundant densities from a density mixture during the learning process. Hence, it can automatically select an appropriate number of densities in density mixture clustering. We experimentally demonstrate its outstanding performance on Gaussian mixtures and color image segmentation problem. Moreover, a simplified version of RPEM generalizes our recently proposed RPCCL algorithm (Y.M. Cheung, 2002) so that it is applicable to elliptical clusters as well with any input proportion. Compared to the existing heuristic RPCL (L. Xu et al., 1993) and its variants, this generalized RPCCL (G-RPCCL) circumvents the difficult preselection of the so-called delearning rate. Additionally, a special setting of the G-RPCCL not only degenerates to RPCL and its Type A variant, but also gives a guidance to choose an appropriate delearning rate for them. Subsequently, we propose a stochastic version of RPCL and its type A variant, respectively, in which the difficult selection problem of delearning rate has been novelly circumvented. The experiments show the promising results of this stochastic implementation.

The existing rival penalized competitive learning (RPCL) algorithm and its variants have provided an attractive way to perform data clustering without knowing the exact number of clusters. However, their performance is sensitive to the preselection of the rival delearning rate. In this paper, we further investigate the RPCL and present a mechanism to control the strength of rival penalization dynamically. Consequently, we propose the rival penalization controlled competitive learning (RPCCL) algorithm and its stochastic version. In each of these algorithms, the selection of the delearning rate is circumvented using a novel technique. We compare the performance of RPCCL to RPCL in Gaussian mixture clustering and color image segmentation, respectively. The experiments have produced the promising results.