Abstract
Recent contrastive multi-view clustering methods have achieved remarkable performance by using two-branch contrastive learning. However, most existing studies focus on the optimization of the false negatives (FNs) identification strategy, ignoring the critical issue of cluster center alignment between fused view and single views. To address this limitation, we present a robust multi-view clustering method (CAFE) based on cross-view adaptive fusion and cluster center enhancement. Specifically, we first design a cross-view adaptive fusion module that incorporates dual weights at both the view level and the sample level, enabling effective coordination of consistent and complementary information across views. Subsequently, we propose a dual-driven cluster center enhancement framework to refine cluster structures. It introduces a dual alignment mechanism between single-view cluster centers and fused-view cluster centers to systematically coordinate view-specific discriminative patterns and cross-view consensus representations. Furthermore, we develop a second-order proximity graph embedding method to more effectively rectify FNs by computing neighborhood similarity. It constructs second-order proximity to identify structurally related samples that may be spatially distant in feature space. Extensive experiments on six widely used multi-view benchmark datasets demonstrate that CAFE achieves state-of-the-art performance under both complete and incomplete multi-view scenarios.