Sample-Level Weighted and Structure-Enhanced Anchor Graph Learning for Scalable Multi-View Clustering

The anchor-based multi-view clustering method has recently attracted considerable attention due to its superior efficiency. However, most existing methods construct a consensus anchor graph based solely on view-level contributions, overlooking the varying importance of individual samples across different views. Moreover, these methods fail to ensure that anchors are evenly distributed across clusters. Thus, we propose a novel and scalable multi-view clustering method, called Sample-Level Weighted and Structure-Enhanced Anchor Graph Learning for Scalable Multi-View Clustering (SLWSE-AGL). Specifically, we introduce a sample-level weighting mechanism based on anchor self-representation learning, enabling the constructed consensus anchor graph to capture the varying importance of samples in different views. Additionally, we incorporate a structure-enhancement constraint to encourage the learned anchors to be more evenly distributed among clusters, leading to more balanced and meaningful cluster partitions. Furthermore, we employ an anchor-to-sample label propagation mechanism that directly yields the final clustering results, thereby avoiding the information loss associated with the two-stage clustering processes. Extensive experiments demonstrate the superior performance of our method compared to state-of-the-art multi-view clustering approaches. The code of SLWSE-AGL is publicly available at https://github.com/tangchuan2000/SLWSE-AGL.