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

arXiv: https://arxiv.org/abs/2607.10413 · HTML: https://mlsanity.com/paper/2607.10413/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 83 references that are in the map.
- First posted on arXiv 11 Jul 2026.

## Builds on (most cited first, 24 of 24)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Multi-level Feature Learning for Contrastive Multi-view Clustering](https://mlsanity.com/paper/2106.11193/) (2021)
- [Deep Partial Multi-View Learning](https://mlsanity.com/paper/2011.06170/) (2020)
- [Doubly Aligned Incomplete Multi-view Clustering](https://mlsanity.com/paper/1903.02785/) (2019)
- [VIGAN: Missing View Imputation with Generative Adversarial Networks](https://mlsanity.com/paper/1708.06724/) (2017)
- [Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment](https://mlsanity.com/paper/2303.15689/) (2023)
- [Generative Partial Multi-View Clustering](https://mlsanity.com/paper/2003.13088/) (2020)
- [Decoupled Contrastive Multi-View Clustering with High-Order Random Walks](https://mlsanity.com/paper/2308.11164/) (2023)
- [Incomplete Multi-view Clustering via Prototype-based Imputation](https://mlsanity.com/paper/2301.11045/) (2023)
- [Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding](https://mlsanity.com/paper/2312.08697/) (2023)
- [A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective](https://mlsanity.com/paper/2309.13989/) (2023)
- [Incomplete Multi-view Clustering via Diffusion Contrastive Generation](https://mlsanity.com/paper/2503.09185/) (2025)
- [Reliable Representation Learning for Incomplete Multi-View Missing Multi-Label Classification](https://mlsanity.com/paper/2303.17117/) (2023)
- [Multi-View Intact Space Learning](https://mlsanity.com/paper/1904.02340/) (2019)
- [On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering](https://mlsanity.com/paper/2303.09877/) (2023)
- [Self-supervised Image Clustering from Multiple Incomplete Views via Constrastive Complementary Generation](https://mlsanity.com/paper/2209.11927/) (2022)
- [Incomplete Multi-view Clustering via Hierarchical Semantic Alignment and Cooperative Completion](https://mlsanity.com/paper/2510.13887/) (2025)
- [Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidance](https://mlsanity.com/paper/2503.11017/) (2025)
- [Dynamic Deep Graph Learning for Incomplete Multi-View Clustering with Masked Graph Reconstruction Loss](https://mlsanity.com/paper/2511.11181/) (2025)
- [Incomplete Multi-view Clustering via Diffusion Completion](https://mlsanity.com/paper/2305.11489/) (2023)
- [URRL-IMVC: Unified and Robust Representation Learning for Incomplete Multi-View Clustering](https://mlsanity.com/paper/2407.09120/) (2024)
- [Learning Disentangled Representations for Generalized Multi-view Clustering](https://mlsanity.com/paper/2605.15640/) (2026)
- [Multi-level Reliable Guidance for Unpaired Multi-view Clustering](https://mlsanity.com/paper/2407.01247/) (2024)

## Abstract (arXiv)

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.