Bipartite contrastive multi-view clustering with singular value modulation

Teng Zhang, Pengyuan Li, Zisen Kong, Dongxia Chang, Yao Zhao

Abstract: Contrastive multi-view clustering (CMvC) has attracted increasing attention for its semantic mining capacity. However, existing CMvC methods often process pairwise views to explore consistency, inevitably ignoring the joint information and inherent redundancy among multiple views. In this paper, we propose a novel Bipartite Contrastive Multi-view Clustering with Singular Value Modulation (BCMVC) that reformulates contrastive learning as a binary classification problem. Specifically, unlike existing pairwise-view sequential processing methods, we construct a correlation learning module that simultaneously mines consistent information across multiple views. This module effectively explores joint information at both the instance level and category level, with each level equipped with a dedicated correlation learner. By leveraging the concat and random shuffle strategy to encapsulate the positive and negative sample sets, the level-specific correlation learner is effectively optimized to enhance the discrimination of samples. Meanwhile, a deep singular value weighting module is introduced to refine the learned representations through a weighted singular value reconstruction strategy, mitigating the adverse effects of noisy information. Extensive experiments on seven benchmark datasets demonstrate that our method achieves substantial advancements compared with other state-of-the-art approaches. The code is available at https://github.com/zhangt-make/BCMVC.

External IDs:dblp:journals/ijon/ZhangLKCZ25