Efficient Anchor-Guided Multi-View Clustering via Diversity–Consistency Learning and Low-Rank Tensor Recovery

Multi-view clustering (MVC) is a fundamental unsupervised learning task for exploring latent structures from heterogeneous multi-view data. Existing MVC methods face critical challenges including the high computational cost of full-graph tensor models, neglect of high-order interactions between diversity and consistency information, and anchor misalignment across different views. In this paper, we propose an efficient anchor-guided MVC framework (EAG-DCT) via diversity–consistency learning and low-rank tensor recovery. The proposed method jointly learns consensus anchors, view-specific diversity graphs, and a global consistency graph in a unified model that integrates all graphs into a high-order tensor to capture rich cross-view correlations. By imposing a nonconvex low-rank constraint on the tensor, we effectively enhance the synergy between diversity and consistency learning. Our framework achieves high computational efficiency and scalability for large-scale data. Comprehensive experimental results on benchmark datasets validate that EAG-DCT outperforms state-of-the-art MVC methods in both clustering effectiveness and efficiency.