Efficient Multi-View Clustering via Essential Tensorized Bipartite Graph Learning

Abstract

Multi-view spectral clustering has garnered significant attention for its capacity to integrate intrinsic feature information from multiple perspectives, resulting in improved performance. However, the oversight of inter-view correlations has led to suboptimal outcomes. Furthermore, the conventional method of constructing an $N \times N$ graph in multi-view clustering imposes a substantial time burden when dealing with large-scale scenarios. To address these challenges, this paper presents an efficient multi-view clustering approach via Essential Tensorized Bipartite Graph Learning (ETBGL). Specifically, ETBGL utilizes the low-rank tensor Schatten $p$-norm to capture inter-view similarity, effectively capturing high-order correlation information embedded in multiple views. Simultaneously, by incorporating bipartite graph learning, ETBGL efficiently mitigates the computational demands and spatial complexity associated with tensor operations. Additionally, we introduce the $\ell _{2,1}$-norm of tensor as a sparse penalty to the error term, with the aim of filtering out noise and preserving shared information, thus enhancing clustering robustness. We solve our objective by an efficient algorithm that is time-economical and has good convergence. Comprehensive evaluations on diverse datasets demonstrate the exceptional performance of our proposed model.