Multi-view Regression Clustering via Low-Rank Manifold Decomposition

Dual-Topology Learning with Adaptive Anchors for Multi-View Clustering

The Dual-tOpology learning with adapTive Anchors (DOTA) is proposed, which not only learns the sample-anchor relationship but also preserves the topology structure among anchors, significantly enhancing the discriminability of learned representation while preserving the underlying data manifold.

Robust multi-view subspace clustering with enhanced high-order correlations

Concept-factorized multi-view clustering with orthogonal diversity and manifold regularization

Efficient Multi-View Clustering Through Pairwise Co-Regularization Non-Negative Matrix Factorization with Sparse Constraints

Recently, multi-view learning techniques based on non-negative matrix factorization (NMF) have demonstrated remarkable efficacy in clustering multi-source data. However, existing methods often overlook the intrinsic similarity between views, and conventional NMF struggles to consistently yield sufficiently sparse solutions. To address these limitations, this paper proposes a novel NMF-based multi-view clustering approach. First, a pairwise co-regularization mechanism is introduced to capture cross-view structural correlations by measuring the similarity between view-specific coefficient matrices. Second, by imposing logarithmic norm sparsity constraints on the coefficient matrix, a pairwise co-regularized NMF model with sparsity constraints is constructed. An efficient iterative update algorithm is derived for the proposed model, and its convergence is rigorously guaranteed in theory. Extensive experiments on eight benchmark datasets validate the effectiveness of the proposed algorithm and its superior sparse representation capability. Furthermore, as a practical engineering optimization, a stochastic acceleration strategy is incorporated to expedite convergence. Empirical results show that this strategy reduces the runtime by approximately 48% on average while preserving clustering performance. Future work will focus on the theoretical underpinnings of this stochastic strategy and its applicability to broader scenarios.

Multi-view graph-regularized deep metric subspace clustering network

Multi-view subspace clustering has progressed significantly by using deep neural networks to handle nonlinear data representations. A recent advancement, the Multi-view Self-Expressive Subspace Clustering (MSESC) network, achieves markedly higher computational efficiency by substituting the traditional self-expression layer with a deep metric learning approach. Nevertheless, MSESC still suffers from two notable limitations: it fails to adequately capture the high-order geometric structures inherent in multi-view data, and it lacks effective guidance from the underlying clustering distribution. To overcome these shortcomings, we propose a novel framework termed Multi-View Graph Regularized Deep Metric Subspace Clustering (MVGR-DMSC). The proposed method introduces two key components into MSESC to enhance the discriminability of representations. First, a dual-order graph regularization module is devised to maintain both first-order and second-order manifold structures, thereby allowing the model to capture more complex local geometric relationships. Second, an adaptive view-weighted deep clustering module is incorporated, which employs the Kullback–Leibler divergence to guide representation learning while dynamically adjusting the contributions of different views. Through evaluations on five benchmark datasets, we show that MVGR-DMSC consistently yields better results than several state-of-the-art approaches, including the direct baseline MSESC, in both accuracy and robustness.

Sparse-Gradient-Regularized Multi-View Clustering with Joint ℓ2,log Sparsity and a Tensor γ* Rank Surrogate

Multi-view clustering seeks a consensus partition from heterogeneous feature views while retaining view-specific information. We propose SGLog-γ∗-MSC, a nonconvex multi-view subspace clustering framework that combines graph total variation, an ℓ2,log penalty, and a tensor γ∗ spectral penalty. These terms promote locally consistent self-representations, model sample-wise corruption, and capture shared low-rank structure across views, respectively. We derive an alternating augmented-Lagrangian algorithm with candidate-selection rules that return global minimizers for both nonconvex proximal subproblems. We also state explicit conditions under which accumulation points satisfy the KKT system and establish conditional whole-sequence convergence through the Kurdyka–Łojasiewicz framework. Hyperparameters are selected by a label-free protocol specified before the audited rerun; ground-truth labels are never used during selection. Across 20 recorded k-means++ initializations of each fixed embedding, the method attains NMI 0.8206±0.0155 on Yale and 0.8962±0.0211 on Scene-15. Relative to nine literature-reported baselines, these are the highest reported NMI values on the two datasets. On UCI digits, BBCSport, and ORL, the method reaches NMI 0.9837, 0.9634, and 0.9892, respectively, ranking second only to HLR-M2VS in the descriptive cross-paper comparison. Component-wise ablation identifies the ℓ2,log term as the largest and most consistent contributor, while the effects of the γ∗ surrogate and sparse-gradient term depend on the dataset. Compared with the tensor nuclear norm, the γ∗ surrogate improves performance on BBCSport, ORL, and UCI digits; sensitivity analysis shows that an interior γ also improves performance on Scene-15. Together, these results support combining robust error modeling with local and shared structural regularization while emphasizing the dataset dependence of individual components.