SEPro: Semantic Evidence Propagation for Incomplete Multi-View Clustering

Incomplete Multi-View Clustering (IMVC) aims to learn coherent representations from data with missing views. However, observed views may also be noisy, biased, or incomplete in semantics, making existing reconstruction- and consistency-based methods vulnerable to unreliable information. To address this issue, we propose SEPro, a semantic evidence propagation framework for IMVC. SEPro represents each view with explicit semantic evidence, uncertainty, and confidence, and propagates this evidence through prototype-mediated high-order relations guided by view availability, semantic affinity, and confidence. The learned evidential representations also support uncertainty-aware inference for missing views. By jointly integrating evidence extraction, propagation, and inference, SEPro learns robust consensus representations for incomplete multi-view clustering. Extensive experiments demonstrate its effectiveness and robustness under varying missing-view conditions.