High-Order Consensus Completion for Incomplete Contrastive Multi-View Clustering

Incomplete contrastive multi-view clustering aims to learn discriminative representations from incomplete multi-view data. However, existing methods still have two issues: 1) Most imputation-based methods mainly rely on local cross-view relations for missing-view recovery, while neglecting high-order relationships among samples, which may lead to unreliable completion. 2) View incompleteness obscures the latent relationships among samples, making it difficult to expand reliable positive pairs beyond same-sample cross-view pairs. To address these issues, we propose a framework named high-order consensus completion for incomplete contrastive multi-view clustering (HCCIMC). Specifically, we design a high-order consensus completion module, which first constructs a global consensus graph from view-specific bipartite graphs. Then, multi-order random walks are performed on the consensus graph to derive high-order neighborhood structures, which are adaptively combined to recover missing-view representations. Furthermore, we propose a multi-order neighborhood consistency contrastive learning module. It models multi-order neighborhood relationships and evaluates their consistency across different views to select reliable neighborhood samples as positives. These two modules mutually enhance each other and help learn robust clustering-friendly representations. Extensive experimental results demonstrate that our method achieves the state-of-the-art clustering performance.