SPORT: Structure-Aware Prototype Disentanglement for Incomplete Multi-View Clustering

Prototype-based incomplete multi-view clustering (IMVC) has recently gained attention for leveraging prototypes to recover missing views. However, existing methods often overlook view-specific semantics in prototypes, fail to preserve cluster-level relational structures, and rely on global prototypes without exploiting local geometric information, resulting in limited multi-view expressiveness and inaccurate imputation. To address these limitations, we propose a novel framework termed **S**tructure-aware **P**r**O**totype disentanglement fo**R** incomplete multi-view clus**T**ering (SPORT), which explicitly disentangles shared and view-specific components of prototypes while preserving cluster-level relational structures. Specifically, we decouple prototypes into orthogonal shared and view-specific components, aligning only shared components to capture consensus semantics while decorrelating view-specific components to preserve complementary information. Meanwhile, a structure-aware contrastive learning mechanism is incorporated to explicitly model cluster-level relationships during cross-view representation learning. Furthermore, a hybrid imputation strategy integrates global prototype matching with local neighborhood matching, enabling joint exploitation of semantic prototypes and manifold structures for missing-view recovery. Extensive experiments on six benchmark datasets show that SPORT achieves superior performance over state-of-the-art methods under various missing rates.