Multi-View Contrastive Learning With Tensor Low-Rank Factorization for Integrated Clustering

Existing multi-view clustering methods often separate feature learning from clustering, leading to suboptimal preservation of global structure and increased reliance on heuristic post-processing. Recently risen tensor-based approaches better model cross-view correlations but still fail to fully capture intrinsic sample relationships. To tackle these shortcomings, we propose a unified one-step multi-view clustering framework (MCOC) that combines contrastive learning, tensor-based self-representation, and nonnegative symmetric matrix factorization. View-specific self-representation matrices are obtained via contrastive learning and arranged along the third mode to form a third-order tensor to capture global consensus across multiple views, then view-specific self-representation matrices are symmetrized to construct affinity graphs. Joint factorization of degree-normalized affinity matrices yields a shared nonnegative indicator matrix, enabling direct cluster assignment. Experiments on real-world datasets validate the superior clustering performance of the proposed method, effectively fusing local view-specific and global structural information.