Energy-preserving shifted bipartite graph learning for unpaired large-scale multi-view clustering.

Abstract

Anchor-based multi-view clustering has advanced significantly due to its accuracy and computational efficiency. However, it relies on the assumption that data across views are fully paired, which is often violated in practice due to asynchronous data collection. This hampers the ability to capture inter-view consistency and complementarity. Few studies have addressed this via bipartite graph alignment, but the challenges about energy disharmony and loss between bipartite graphs caused by unpaired and unbalanced anchors remain unexplored. To address this challenge, this study develops a novel framework named Energy-preserving Shifted Bipartite Graph Learning (ESBGL) for unpaired large-scale multi-view clustering to ensure energy harmony and preservation. Specifically, ESBGL simultaneously proposes anchor alignment learning and bipartite graph learning techniques to align anchors and bipartite graphs across views, which could avoid energy disharmony between bipartite graphs. Subsequently, a shifted bipartite graph learning paradigm is designed to preserve the cluster information and significant energy of the final consensus bipartite graph. Extensive experiments and evaluations have proved the effectiveness and superiority of our ESBGL.