Topology-Aware Contrastive Anchors for Incomplete multi-view clustering

Incomplete multi-view clustering remains challenging because missing observations disrupt cross-view correspondence and distort the underlying structural relationships among samples. We propose Topology-Aware Contrastive Anchors (TACA), a reconstruction-free framework that learns a unified representation directly from incomplete multi-view data through anchor-based contrastive modeling. Instead of explicitly recovering missing features, TACA constructs view-specific anchor representations and aligns their semantic information across views, thereby reducing the dependence on unreliable feature imputation. To further preserve structural information beyond a single anchor resolution, we introduce a topology-aware cross-scale consistency mechanism that coordinates multi-scale anchor graphs by encouraging agreement among their latent relational structures. Moreover, incomplete observations may generate semantically ambiguous negative pairs and weaken contrastive supervision. TACA addresses this issue through a negative sample decoupling strategy that suppresses unreliable negatives according to their semantic similarity. These components are jointly optimized to obtain discriminative and structurally consistent representations. Extensive experiments on eight benchmark datasets under different missing rates demonstrate that TACA consistently achieves competitive clustering performance against recent incomplete multi-view clustering methods while maintaining linear complexity with respect to the number of samples.