Efficient Multi-view Clustering via Unified and Discrete Bipartite Graph Learning

• URL: http://arxiv.org/abs/2209.04187v2
• Date: Wed, 22 Mar 2023 15:19:17 GMT
• Title: Efficient Multi-view Clustering via Unified and Discrete Bipartite Graph Learning
• Authors: Si-Guo Fang, Dong Huang, Xiao-Sha Cai, Chang-Dong Wang, Chaobo He, Yong Tang
• Abstract summary: This paper presents an efficient multi-view clustering approach via unified and discrete bipartite graph learning (UDBGL) An anchor-based subspace learning is incorporated to learn the view-specific bipartite graphs from multiple views. The Laplacian rank constraint is imposed to ensure that the fused bipartite graph has discrete cluster structures.
• Score: 15.617206773324952
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although previous graph-based multi-view clustering algorithms have gained significant progress, most of them are still faced with three limitations. First, they often suffer from high computational complexity, which restricts their applications in large-scale scenarios. Second, they usually perform graph learning either at the single-view level or at the view-consensus level, but often neglect the possibility of the joint learning of single-view and consensus graphs. Third, many of them rely on the k-means for discretization of the spectral embeddings, which lack the ability to directly learn the graph with discrete cluster structure. In light of this, this paper presents an efficient multi-view clustering approach via unified and discrete bipartite graph learning (UDBGL). Specifically, the anchor-based subspace learning is incorporated to learn the view-specific bipartite graphs from multiple views, upon which the bipartite graph fusion is leveraged to learn a view-consensus bipartite graph with adaptive weight learning. Further, the Laplacian rank constraint is imposed to ensure that the fused bipartite graph has discrete cluster structures (with a specific number of connected components). By simultaneously formulating the view-specific bipartite graph learning, the view-consensus bipartite graph learning, and the discrete cluster structure learning into a unified objective function, an efficient minimization algorithm is then designed to tackle this optimization problem and directly achieve a discrete clustering solution without requiring additional partitioning, which notably has linear time complexity in data size. Experiments on a variety of multi-view datasets demonstrate the robustness and efficiency of our UDBGL approach. The code is available at https://github.com/huangdonghere/UDBGL.

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