Contrastive learning for deep multi-view clustering aims to learn discriminative representations across multiple views. However, prevailing cluster-level alignment approaches fail to fully leverage cross-view consistency and complementarity, as they neglect instance-level semantic coherence. To address this limitation, we propose a novel bidirectional contrastive learning network for multi-view clustering. By simultaneously contrasting the inter-view semantic label matrix along the row and column directions (i.e., at instance-level and cluster-level), the labels of the same instance in different views are consistent, and instances assigned to the same cluster across different views remain consistent. Moreover, we use dual-channel MLPs to avoid information conflicts caused by bidirectional contrastive learning. The proposed framework also demonstrates strong generalization capability, serving as a plug-and-play module that can be seamlessly integrated with existing methods to improve their clustering performance. Extensive experiments on publicly available datasets demonstrate the superiority of our method over several state-of-the-art techniques.

Multi-view clustering based on deep auto-encoder networks has garnered increasing attention and made significant progress in recent years. However, we argue that most existing methods inadequately explore the discriminability while learning clustering assignments, resulting in models struggling to accurately cluster data, particularly those with ambiguous semantics. To address this problem, we propose a novel framework termed deep discriminative multi-view clustering (DDMvC). This framework is designed to further increase the inter-cluster distances by learning a discriminative projection dictionary with global prior information. To begin with, we enhance the reliability of the dictionary atoms by initializing them with class-specific prototypes derived from concatenated global features across multiple views. Subsequently, we iteratively refine the atoms to guarantee their independence from any specific cluster. Simultaneously, we incorporate contrastive learning for the cluster assignments projected by these atoms, striving for inter-view consistent clustering results. Experimental results on benchmark multi-view datasets demonstrate that our framework achieves the state-of-the-art clustering performance.

Multi-view clustering aims at partitioning data into their underlying categories by mining shared and complementary information conveyed by different views. Although the integration of deep learning and disentanglement learning has markedly improved clustering performance, our analysis reveals two fundamental limitations in existing approaches: inadequate separation between view-shared and view-exclusive features; and the negative effects of clustering-irrelevant information on feature decoupling. To tackle these issues, we present a novel Disentangled Feature Learning Network (DFL-Net), which utilizes a progressive learning framework to systematically disentangle features. DFL-Net initially establishes view-shared representations through semantic disparity minimization, followed by the construction of orthogonal feature subspaces using cross-view and intra-view independence constraints to isolate view-specific features. Subsequently, DFL-Net enforces clustering consistency across views to adaptively eliminate irrelevant information, thus enhancing the overall effectiveness of disentanglement learning. The framework introduces two significant innovations: a comprehensive feature independence criterion that concurrently reduces intra-view and cross-view feature dependencies, and an irrelevance filtering mechanism that ensures cross-view clustering consistency. Extensive experiments on benchmark datasets demonstrate the superior performance of DFL-Net compared to state-of-the-art methods.

Multi-view subspace clustering is an important topic in cluster analysis. Its aim is to utilize the complementary information conveyed by multiple views of objects to be clustered. Recently, view-shared anchor learning based multi-view clustering methods have been developed to speed up the learning of common data representation. Although widely applied to large-scale scenarios, most of the existing approaches are still faced with two limitations. First, they do not pay sufficient consideration on the negative impact caused by certain noisy views with unclear clustering structures. Second, many of them only focus on the multi-view consistency, yet are incapable of capturing the cross-view diversity. As a result, the learned complementary features may be inaccurate and adversely affect clustering performance. To solve these two challenging issues, we propose a Fast Self-guided Multi-view Subspace Clustering (FSMSC) algorithm which skillfully integrates the view-shared anchor learning and global-guided-local self-guidance learning into a unified model. Such an integration is inspired by the observation that the view with clean clustering structures will play a more crucial role in grouping the clusters when the features of all views are concatenated. Specifically, we first learn a locally-consistent data representation shared by all views in the local learning module, then we learn a globally-discriminative data representation from multi-view concatenated features in the global learning module. Afterwards, a feature selection matrix constrained by the ℓ2,1-norm is designed to construct a guidance from global learning to local learning. In this way, the multi-view consistent and diverse information can be simultaneously utilized and the negative impact caused by noisy views can be overcame to some extent. Extensive experiments on different datasets demonstrate the effectiveness of our proposed fast self-guided learning model, and its promising performance compared to both, the state-of-the-art non-deep and deep multi-view clustering algorithms. The code of this paper is available at https://github.com/chenzhe207/FSMSC.

The dictionary pair learning (DPL) model aims to design a synthesis dictionary and an analysis dictionary to accomplish the goal of rapid sample encoding. In this article, we propose a novel structured representation learning algorithm based on the DPL for image classification. It is referred to as discriminative DPL with scale-constrained structured representation (DPL-SCSR). The proposed DPL-SCSR utilizes the binary label matrix of dictionary atoms to project the representation into the corresponding label space of the training samples. By imposing a non-negative constraint, the learned representation adaptively approximates a block-diagonal structure. This innovative transformation is also capable of controlling the scale of the block-diagonal representation by enforcing the sum of within-class coefficients of each sample to 1, which means that the dictionary atoms of each class compete to represent the samples from the same class. This implies that the requirement of similarity preservation is considered from the perspective of the constraint on the sum of coefficients. More importantly, the DPL-SCSR does not need to design a classifier in the representation space as the label matrix of the dictionary can also be used as an efficient linear classifier. Finally, the DPL-SCSR imposes the$l_{2,p}$-norm on the analysis dictionary to make the process of feature extraction more interpretable. The DPL-SCSR seamlessly incorporates the scale-constrained structured representation learning, within-class similarity preservation of representation, and the linear classifier into one regularization term, which dramatically reduces the complexity of training and parameter tuning. The experimental results on several popular image classification datasets show that our DPL-SCSR can deliver superior performance compared with the state-of-the-art (SOTA) dictionary learning methods. The MATLAB code of this article is available athttps://github.com/chenzhe207/DPL-SCSR.

We propose a novel structured analysis–synthesis dictionary pair learning method for efficient representation and image classification, referred to as relaxed block-diagonal dictionary pair learning with a locality constraint (RBD-DPL). RBD-DPL aims to learn relaxed block-diagonal representations of the input data to enhance the discriminability of both analysis and synthesis dictionaries by dynamically optimizing the block-diagonal components of representation, while the off-block-diagonal counterparts are set to zero. In this way, the learned synthesis subdictionary is allowed to be more flexible in reconstructing the samples from the same class, and the analysis dictionary effectively transforms the original samples into a relaxed coefficient subspace, which is closely associated with the label information. Besides, we incorporate a locality-constraint term as a complement of the relaxation learning to enhance the locality of the analytical encoding so that the learned representation exhibits high intraclass similarity. A linear classifier is trained in the learned relaxed representation space for consistent classification. RBD-DPL is computationally efficient because it avoids both the use of class-specific complementary data matrices to learn discriminative analysis dictionary, as well as the time-consuming$l_{1}/l_{0}$-norm sparse reconstruction process. The experimental results demonstrate that our RBD-DPL achieves at least comparable or better recognition performance than the state-of-the-art algorithms. Moreover, both the training and testing time are significantly reduced, which verifies the efficiency of our method. The MATLAB code of the proposed RBD-DPL is available athttps://github.com/chenzhe207/RBD-DPL.

We propose an alternating deep-layer cascade (A-DLC) architecture for representation learning in the context of image classification. The merits of the proposed model are threefold. First, A-DLC is the first-ever method that alternatively cascades the sparse and collaborative representations using the class-discriminant softmax vector representation at the interface of each cascade section so that the sparsity and collaborativity can simultaneously be considered. Second, A-DLC inherits the hierarchy learning capability that effectively extends the traditional shallow sparse coding to a multi-layer learning model, thus enabling a full exploitation of the inherent latent discriminative information. Third, the simulation results show a significant amelioration in the classification accuracy, compared to earlier one-step single-layer classification algorithms. The Matlab code of this paper is available at https://github.com/chenzhe207/A-DLC.