MambaMVC: Learning state space model enhanced representation for contrastive multi-view clustering.

Abstract

Deep multi-view clustering has been extensively studied due to its good ability to capture deep features and handle high-dimensional data. However, existing methods remain subject to the following shortcomings: (1) They typically employ single-layer fusion, which hinders the ability of fused features to comprehensively cover critical information; (2) They ignore the complex dependencies between views, thereby constraining the expressiveness of local features. We address these problems by proposing MambaMVC, a powerful state space model enhanced representation model for Multi-view Clustering. Specifically, we design dual-layer feature fusion to capture more comprehensive global information. First, we introduce a global structure-guided feature fusion learning module (GSFL) to conduct sample-level fusion as the first-layer stage. In the second-layer fusion, we design the global bi-directional Mamba module (GB-Mamba) to capture more intricate long-range dependencies between samples, which mitigate the information discrepancies between the preliminary global embedding feature and the view-specific embedding features. Meanwhile, we use the cross-view bi-directional Mamba module (CB-Mamba) to enhance information exchange between views, thus obtaining more comprehensive local information. Furthermore, contrastive learning is utilized to facilitate label consistency across cluster distribution, effectively reducing the influence of misleading information in specific views. Finally, we utilize the global target distribution to effectively guide the optimization of the cluster distribution in self-supervised learning. Experimental results on eight multi-view datasets demonstrate the effectiveness of the proposed model. Our code is publicly available at .