Pseudo-Label Similarity Graph-Driven Multi-View Contrastive Clustering

Multi-view clustering has exhibited exceptional performance by harnessing complementary information and extracting shared semantics across diverse views. However, existing methods still encounter substantial obstacles in exploiting high-quality consensus representation: (i) they typically lack effective guidance mechanisms under unsupervised conditions, leading to suboptimal representation learning, and (ii) they overlook the intrinsic cluster structure when forming sample pairs to align representation across views, conflicting with clustering objectives. To address these issues, we propose a contrastive-based method calledPseudo-LabelSimilarityGraph-driven Multi-ViewClustering (PSGVC). Specifically, to uncover the potential semantic relationships between samples, we construct a pseudo-label similarity graph based on the consensus representation. Then, we design a similarity graph-based contrastive loss, which utilizes the pseudo-label similarity graph as a global supervision signal to guide the optimization of cross-view embedded representation, thereby indirectly improving the quality of consensus representation. Additionally, we propose a weighted cluster-aware contrastive loss to align the consensus representation with view-specific representation. It leverages the cluster structure contained in the pseudo-label similarity graph to achieve cluster-level contrastive learning, further enhancing the quality of the consensus representation. Extensive experimental results show that our PSGVC achieves state-of-the-art clustering performance across multiple datasets.