Structure Diversity-Induced Anchor Graph Fusion for Multi-View Clustering

Abstract

The anchor graph structure has been widely used to speed up large-scale multi-view clustering and exhibited promising performance. How to effectively integrate the anchor graphs on multiple views to achieve enhanced clustering performance still remains a challenging task. Existing fusing strategies ignore the structure diversity among anchor graphs and restrict the anchor generation to be same on different views, which degenerates the representation ability of corresponding fused consensus graph. To overcome these drawbacks, we propose a novel structural fusion framework to integrate the multi-view anchor graphs for clustering. Different from traditional integration strategies, we merge the anchors and edges of all the view-specific anchor graphs into a single graph for the structural optimal graph learning. Benefiting from the structural fusion strategy, the anchor generation of each view is not forced to be same, which greatly improves the representation capability of the target structural optimal graph, since the anchors of each view capture the diverse structure of different views. By leveraging the potential structural consistency among each anchor graph, a connectivity constraint is imposed on the target graph to indicate clusters directly without any post-processing such as k -means in classical spectral clustering. Substantial experiments on real-world datasets are conducted to verify the superiority of the proposed method, as compared with the state-of-the-arts over the clustering performance and time expenditure.