Prototype‑Driven Reliability‑Aware Semantic Consensus Learning for Incomplete Multi‑View Clustering

Incomplete multi-view clustering (IMVC) faces challenges from missing views, heterogeneous view distributions, and unequal view quality, making it difficult to learn reliable and consistent semantic consensus representations. To address these issues, we propose a Prototype-driven Reliability-aware Semantic Consensus Learning (PRSCL) framework, which centers on consensus semantic prototypes to align semantic representations across different views and ultimately achieves the fusion of reliable semantic representations. Specifically, PRSCL first employs a reliability-aware gating mechanism to adaptively evaluate the quality of available views and perform instance-level weighted fusion, thereby producing reliable consensus semantic representations and semantic prototypes. PRSCL then employs entropy-regularized optimal transport to derive reliable soft assignments between samples and consensus semantic prototypes, while enforcing cross-view consistency at the clustering level through bidirectional mutual supervision. By jointly modeling feature consistency, view reliability, and prototype-driven semantic structure, PRSCL learns robust consensus semantic representations without explicitly imputing missing views. Extensive experiments on benchmark datasets demonstrate its effectiveness under various missing-view conditions.