Deep Similarity Graph Fusion for Multiview Clustering

Abstract

The graph-based multiview clustering has gained significant attention due to its effectiveness in representing complex relationships among multiview data for enhanced clustering. Among the previous graph-based methods, the multiview graph learning (or graph fusion) technique has rapidly emerged as a promising direction, which, however, still suffers from two critical limitations. First, most of previous methods adopt a single-level of graph fusion, which lack the ability to go from single-level graph fusion to multilevel (deep) graph fusion. Second, they generally focus on constructing an optimal unified graph but cannot fully investigate the correlations among multiple views. Therefore, it is difficult to establish a comprehensive and obvious graph structure. In light of this, this article presents a new multiview graph learning method called deep similarity graph fusion (DSGF) for the multiview clustering task, where three pathways are simultaneously leveraged to fuse multilevel similarity into a unified graph. Particularly, multilevel graph fusion is utilized to obtain a view-specific similarity graph for each view and then fuse these single-view graphs (via three levels of graph fusion) into a robust graph, which takes advantage of deeper consensus information between various similarity graphs and improves the quality of the learned graph for the final spectral clustering process. Extensive experiments are conducted on six real-world multiview datasets, which demonstrate the highly competitive clustering performance of DSGF in comparison with state-of-the-art methods.