Learning from Disjoint Views: A Contrastive Prototype Matching Network for Fully Incomplete Multi-View Clustering
Yiming Wang, Qun Li, Dongxia Chang, Jie Wen, Hua Dai, Fu Xiao, Yao Zhao
Multi-view clustering aims to enhance clustering performance by leveraging information from diverse sources. However, existing methods typically assume that instances are present in most or all views, which is impractical in real-world scenarios. This paper focuses on the understudied problem of fully incomplete multi-view clustering (FIMC), where each instance contains only a single view or cross-view correspondences are entirely unknown. To address this problem, we propose a Contrastive Prototype Matching Network (CPMN), which pioneers an explicit prototype matching strategy to effectively align cross-view instances through category-level correspondence. CPMN first employs a correspondence-free graph contrastive learning approach, leveraging mutual $k$-nearest neighbors (MNN) to uncover structural correlations and establish initial view-specific prototypes from entirely unpaired views. Building on the prototypes, we introduce a cross-view prototype graph matching stage to resolve category misalignment and forge a unified clustering structure. Finally, guided by this alignment, we devise a prototype-aware contrastive learning mechanism to promote semantic consistency, replacing the reliance on the initial MNN-based structural similarity. Extensive experiments on benchmark datasets demonstrate that our method significantly outperforms various baselines and ablation variants, validating its effectiveness.