Robust and Consistent Anchor Graph Learning for Multi-View Clustering

Anchor-based multi-view graph clustering has recently gained popularity as an effective approach for clustering data with multiple views. However, existing methods have limitations in terms of handling inconsistent information and noise across views, resulting in an unreliable consensus representation. Additionally, post-processing is needed to obtain final results after anchor graph construction, which negatively affects clustering performance. In this paper, we propose a Robust and Consistent Anchor Graph Learning method (RCAGL) for multi-view clustering to address these challenges. RCAGL constructs a consistent anchor graph that captures inter-view commonality and filters out view-specific noise by learning a consistent part and a view-specific part simultaneously. A $k$ -connectivity constraint is imposed on the consistent anchor graph, leading to a clear graph structure and direct generation of cluster labels without additional post-processing. Experimental results on several benchmark datasets demonstrate the superiority of RCAGL in terms of clustering accuracy, scalability to large-scale data, and robustness to view-specific noise, outperforming advanced multi-view clustering methods. Our code is publicly available at https://github.com/Tracesource/RCAGL .