Pseudo-Correspondence Guided Blind-Alignment Learning for Completely Unaligned Multi-View Clustering

Multi-view clustering aims to exploit complementary and consistent information from heterogeneous views to discover a shared clustering structure without supervision. However, when cross-view instance correspondences are entirely unavailable, completely unaligned multi-view clustering faces three key challenges: unreliable local structures may hinder early correspondence discovery; correspondence estimation and representation learning are mutually dependent; and exact instance matching alone is insufficient to capture cluster-level semantics. To address these challenges, we propose Pseudo-Correspondence Guided Blind-Alignment Learning (PCGBAL), which treats cross-view alignment as an evolving latent-supervision process coupled with representation learning. PCGBAL first structurally bootstraps view-specific representations through masked topological reconstruction and then progressively discovers high-confidence pseudo-correspondences from the evolving representation space. These associations are fed back as latent supervision to refine heterogeneous representations, forming a mutually reinforcing correspondence–representation process. To further bridge the gap between instance-level correspondence and clustering semantics, instance-to-cluster contrastive alignment transfers cluster-level information across views beyond exact instance matches. Extensive experiments on eight benchmark datasets against seven competing methods demonstrate the effectiveness and robustness of PCGBAL in completely unaligned settings.