Fast Multi-View Subspace Clustering Based on Flexible Anchor Fusion

Multi-view subspace clustering enhances clustering performance by optimizing and integrating structural information from multiple views. Recently, anchor-based methods have made notable progress in large-scale clustering scenarios by leveraging anchor points to capture data distribution across different views. Although these methods improve efficiency, a common limitation is that they typically select an equal number of anchor points from each view. Additionally, during the graph fusion stage, most existing frameworks use simple linear weighting to construct the final consensus graph, overlooking the inherent structural relationships between the data. To address these issues, we propose a novel and flexible anchor graph fusion framework which selects an appropriate number of anchor points for each view based on its data space, creating suitable anchor graphs. In the graph fusion stage, we introduce a regularization term which adaptively and flexibly combines anchor graphs of varying sizes. Moreover, our approach incorporates both global and local information between views, enabling a more accurate capture of the cluster structure within the data. Furthermore, our method operates with linear time complexity, making it well suited for large-scale datasets. Extensive experiments on multiple datasets demonstrate the superior performance of our proposed algorithm.