Embedded Multi-View Clustering via Collaborative Tensor Subspace Representation and Multi-Graph Fusion

Multi-view clustering (MVC) strives to reveal the hidden correlations and potential distribution of data from multiple views. However, most existing methods separate feature extraction and clustering processes, relying heavily on pre-learning and post-processing, leading to information loss and suboptimal clustering results. Therefore, we propose a novel embedded multi-view clustering model EMVCTM via collaborative tensor subspace representation and adaptive multi-graph fusion. The tensor self-representation framework is designed to capture global structural information. Symmetry constraints are relaxed synchronously to adaptively learn view-specific affinity graphs and dynamically generate fusion graph. The near-global optimal clustering result is derived through an efficient embedded clustering framework with activated view interactions, providing a more interpretable explanation for cluster division. Experimental results on various real-world datasets confirm that EMVCTM outperforms existing state-of-the-art techniques.