Scalable One-step Unaligned Multi-view Clustering via Joint High-Order Correlation Learning

Traditional multi-view clustering methods rely on the cross-view sample alignment presumption to explore consistent and complementary information from multiple views. However, in real-world scenarios, sensor heterogeneity and decentralized data storage and processing frequently make this presumption violated, leading to the Unaligned Multi-view Clustering (UMC) problem. Although existing works have promoted the development of UMC, they have at least one of the following limitations, i.e., high computational complexity, inadequate use of high-order correlation and two-stage clustering. To address these limitations, we propose a Joint High-order Correlation Learning (JHCL) framework for scalable one-step unaligned multi-view clustering. Specifically, multi-order bipartite graphs are utilized to make fully use of intra-view high-order correlations. Then, based on a tensorial bipartite graph alignment and fusion model, inter-view high-order correlations are exploited simultaneously. In such manner, the learned consistent bipartite graph retains adequate structural information for accurate and fast clustering in one step. Extensive experiments on real-world datasets validate the superiority of JHCL in both clustering performance and computational efficiency. Code available: https://github.com/revolution6575/JHCL.git.