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

• URL: http://arxiv.org/abs/2607.10413v1
• Date: Sat, 11 Jul 2026 17:40:22 GMT
• Title: SPORT: Structure-Aware Prototype Disentanglement for Incomplete Multi-View Clustering
• Abstract summary: Prototype-based Incomplete Multi-view Clustering has attracted increasing attention by exploiting prototypes as semantic anchors for missing-view imputation.<n>We propose a novel framework termed Structure-aware PrOtotype disentanglement foR incomplete multi-view clusTering (SPORT)<n>SPORT explicitly disentangles shared and view-specific components of prototypes while preserving cluster-level relational structures.
• Score: 51.01038967474103
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prototype-based Incomplete Multi-view Clustering has recently attracted increasing attention by exploiting prototypes as semantic anchors for missing-view imputation. However, existing approaches are still limited in three aspects. First, they typically focus on enforcing cross-view prototype consistency, while ignoring view-specific information embedded in prototypes, thus limiting multi-view expressiveness. Second, most methods rely on instance-level contrastive learning that only aligns paired samples across views, failing to preserve cluster-level relational structures. Third, missing-view imputation is usually performed using global prototypes alone, without considering local geometric neighborhood structures, leading to inaccurate recovery of missing representations. To address these limitations, we propose a novel framework termed Structure-aware PrOtotype disentanglement foR incomplete multi-view clusTering (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 de-correlating 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.

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