Scalable unpaired multi-view clustering with Bipartite Graph Matching

Relying on the assumption of view pairing, anchor-based multi-view clustering has been highly effective in handling large-scale datasets. Whereas, during data collection and transmission of many real-world applications, various issues such as asynchronous Internet of Things sensors and surveillances or asynchronous Alzheimer diagnosis data can disrupt the pairing assumption in multi-view data, causing Sample Unpaired Problem (SUP). This SUP escalates into an even greater challenge in large-scale clustering tasks. To overcome this challenge, we propose a Scalable Unpaired Multi-view Clustering with Bipartite Graph Matching (SUMC-BGM). SUMC-BGM has devised a novel bipartite graph matching framework to learn a consistent structure bipartite graph for clustering from large-scale unpaired data. This framework primarily addresses two challenges: (1) To solve anchor misalignment, we first propose the desired anchor alignment learning paradigm to ensure the alignment, fairness, compactness, and diversity of anchors. (2) To address edge misalignment, we further propose an edge alignment learning scheme to ensure consistency in the bipartite graph structure of the learned view-specific edges. To the best of our knowledge, SUMC-BGM represents the pioneering endeavor to address the less-touched large-scale unpaired challenge. Extensive experiments verify the superiority, validity, and efficiency of SUMC-BGM compared with 22 state-of-the-art competitors on the 13 benchmark datasets.