DPFMVC: Dynamic Progressive Fusion for Multi-view Clustering

Multi-view clustering aims to effectively integrate data from multiple views to uncover the underlying clustering structure. However, existing methods typically adopt direct fusion strategies for multiview data, neglecting the issues of view gap-induced heterogeneity and the imbalance in view quality. Particularly, when there are significant differences between views, such direct fusion often leads to the loss of critical information and a decline in clustering performance. To address these challenges, we propose a novel Dynamic Progressive Fusion Multi-View Clustering (DPFMVC). DPFMVC employs a view-adaptive fusion mechanism that dynamically selects the most similar views, reducing conflicts between views while preserving complementary information. Additionally, DPFMVC introduces a dual contrastive loss module and a progressive fusion loss, which effectively align sample features with clustering centers, promoting efficient integration of multi-view information. Specifically, the dual contrastive loss compares the similarity between sample features and cluster centers, ensuring cross-view feature consistency and thus enhancing the discriminability of clustering. Meanwhile, the progressive fusion loss progressively adjusts the fusion order of views, effectively reducing the negative impact of low-quality views on the clustering results, strengthening the synergy between views, and facilitating more effective information fusion.Comprehensive experiments on multiple public benchmarks show that DPFMVC delivers superior clustering results and exhibits overall great effectiveness compared to state-of-the-art techniques.