Structure-Aware Conditional Drift Generation for Incomplete Multi-View Clustering

Incomplete multi-view clustering (IMVC) aims to discover latent structures from multi-view data with partially unavailable observations. However, existing methods often recover missing information through feature estimation, cross-view averaging, or neighborhood transfer, which may overlook the target-view distribution and weaken sample correspondence. Although recent approaches improve missing-view recovery by exploiting cross-view consistency or local relationships, they generally provide limited joint modeling of local geometry and cluster-level semantics, while the reliability of recovered representations is rarely considered during subsequent fusion. To tackle these issues, we propose Structure-Aware Conditional Drift Generation (SACDG) for IMVC. Specifically, SACDG takes shared semantics from observed views as a reliable cross-view reference and learns a structure-guided drift toward the target-view distribution, thereby preserving sample correspondence while adapting to target-specific geometry and cluster semantics. Additionally, pseudo-missing self-supervision estimates recovery reliability to reduce the influence of inaccurate completions during multi-view fusion. Finally, structural alignment and prototype-driven clustering provide consistent feedback for representation recovery and clustering. Extensive experiments on seven benchmark datasets under multiple missing rates demonstrate the effectiveness and robustness of SACDG compared with state-of-the-art IMVC methods.