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.