Unaligned multi-view clustering via diversified anchor graph fusion

Abstract

Clear sample correspondence across views is a key presupposition of traditional multi-view clustering. However, in practical applications, uncertainties during the data collection process may lead to the violation of this presupposition, producing unaligned multi-view data. In this paper, to overcome the obstacle of multi-view fusion caused by unaligned samples and achieve efficient unaligned multi-view clustering, a novel Diversified Anchor Graph Fusion (DAGF) method is proposed. Specifically, view-specific bipartite graphs with diversified anchors are constructed to adapt to the characteristics of unaligned multi-view data. Then, with the devised sample alignment and anchor integration strategy, these bipartite graphs are fused to learn a joint bipartite graph with explicit cluster membership structure. The proposed DAGF method not only overcomes the adverse effects of unaligned samples on cross-view information fusion, but also preserves complementary view-specific clustering structure information, enabling efficient and effective clustering. Systematic experimental results on real-world datasets demonstrate the advantages of the DAGF method in both clustering performance and computational complexity. Code available: https://github.com/revolution6575/DAGF.git.