Propagating Cross-View Semantics for Multi-view Clustering: A Unified Anchor Refinement Paradigm.

Abstract

Anchor-based methods have demonstrated significant success in multi-view clustering, particularly in handling large-scale datasets. However, existing approaches suffer from two critical limitations: (1) clustering performance is highly sensitive to the quality of initial anchors, and (2) multi-view interactions are often limited to the anchor graph construction stage, resulting in semantic misalignment among independently generated anchors. To address these challenges, we propose a unified paradigm based on Cross-View Semantic Propagation (CVSP), a plug-and-play framework that effectively improves anchor quality. Depending on whether the initial anchors are fixed or learnable, this strategy is instantiated in two variants, CVSP-F for fixed anchors and CVSP-U for learnable anchors with iterative updates. Specifically, the anchor alignment module first ensures cross-view consistency among the initial anchors. Then, the anchor refinement module, leveraging a cross-view graph, optimizes the anchor representations. Finally, a revised anchor graph construction module is introduced to further improve clustering robustness. Experimental results demonstrate that both CVSP-F and CVSP-U consistently outperform twelve state-of-the-art anchor-based methods across multiple datasets. Furthermore, extensive evaluations confirm the generalization capability of the anchor refinement module when integrated with various existing methods.