BCN: Bidirectional Contrastive Learning Net for Multi-View Clustering

Contrastive learning for deep multi-view clustering aims to learn discriminative representations across multiple views. However, prevailing cluster-level alignment approaches fail to fully leverage cross-view consistency and complementarity, as they neglect instance-level semantic coherence. To address this limitation, we propose a novel bidirectional contrastive learning network for multi-view clustering. By simultaneously contrasting the inter-view semantic label matrix along the row and column directions (i.e., at instance-level and cluster-level), the labels of the same instance in different views are consistent, and instances assigned to the same cluster across different views remain consistent. Moreover, we use dual-channel MLPs to avoid information conflicts caused by bidirectional contrastive learning. The proposed framework also demonstrates strong generalization capability, serving as a plug-and-play module that can be seamlessly integrated with existing methods to improve their clustering performance. Extensive experiments on publicly available datasets demonstrate the superiority of our method over several state-of-the-art techniques.