Consensus Low-Rank Multi-View Subspace Clustering With Cross-View Diversity Preserving
Kehan Kang, Chenglizhao Chen, Chong Peng · IEEE Signal Processing Letters · 2023
Multi-view subspace clustering has drawn significant attentions in recent years, which significantly improves learning performance of the single-view methods. In this letter, we propose a novel multi-view subspace clustering method, which learns a consensus representation with auto-weighted local neighboring transition probability matrix fusion and preserves cross-view diversity with a matrix-induced term. The new model is convex and thus admits efficient optimization. The effectiveness is confirmed by extensive experiments.