Unified and efficient multi-view clustering with tensorized bipartite graph

A considerable amount of multi-view subspace clustering (MVSC) algorithms have been investigated to explore widely available multi-view data. Among these methods, anchor-based MVSC algorithms stand out for their effectiveness and efficiency in handling large-scale data. However, the following two limitations lead to inferior performance: (1) a lack of consideration for the high-order correlations of bipartite graphs; (2) a disjointed process that independently executes anchor selection, bipartite graph learning, and spectral embedding. To handle these drawbacks, we propose an unified framework that allows for jointly learning consensus anchor matrix and tensorized bipartite graph, as well as integrating a fast spectral embedding technique. We name our method as Unified and Efficient Multi-View Clustering with Tensorized Bipartite Graph (UEMC-TBG). Specifically, UEMC-TBG captures the high-order correlations of multiple bipartite graphs with consensus anchors. This is achieved by minimizing the tensor-Singular Value Decomposition (t-SVD) based tensor nuclear norm. Furthermore, we innovatively incorporate a fast spectral embedding technique for bipartite graph. Extensive experiments on eight datasets show that UEMC-TBG provides better performance than advanced baselines. One can access the source code on https://github.com/lshi91/UEMC-TBG.