Learning Common Semantics via Optimal Transport for Contrastive Multi-View Clustering

Qian Zhang, Lin Zhang, Ran Song, Runmin Cong, Yonghuai Liu, Wei Zhang

Abstract: Multi-view clustering aims to learn discriminative representations from multi-view data. Although existing methods show impressive performance by leveraging contrastive learning to tackle the representation gap between every two views, they share the common limitation of not performing semantic alignment from a global perspective, resulting in the undermining of semantic patterns in multi-view data. This paper presents CSOT, namely Common Semantics via Optimal Transport, to boost contrastive multi-view clustering via semantic learning in a common space that integrates all views. Through optimal transport, the samples in multiple views are mapped to the joint clusters which represent the multi-view semantic patterns in the common space. With the semantic assignment derived from the optimal transport plan, we design a semantic learning module where the soft assignment vector works as a global supervision to enforce the model to learn consistent semantics among all views. Moreover, we propose a semantic-aware re-weighting strategy to treat samples differently according to their semantic significance, which improves the effectiveness of cross-view contrastive representation learning. Extensive experimental results demonstrate that CSOT achieves the state-of-the-art clustering performance.