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arXiv · 2505.11182

Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning

Yuzhuo Dai·Jiaqi Jin·Zhibin Dong·Siwei Wang·Xinwang Liu·En Zhu·Xihong Yang·Xinbiao Gan·Yu Feng
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Abstract

In incomplete multi-view clustering (IMVC), missing data induce prototype shifts within views and semantic inconsistencies across views. A feasible solution is to explore cross-view consistency in paired complete observations, further imputing and aligning the similarity relationships inherently shared across views. Nevertheless, existing methods are constrained by two-tiered limitations: (1) Neither instance- nor cluster-level consistency learning construct a semantic space shared across views to learn consensus semantics. The former enforces cross-view instances alignment, and wrongly regards unpaired observations with semantic consistency as negative pairs; the latter focuses on cross-view cluster counterparts while coarsely handling fine-grained intra-cluster relationships within views. (2) Excessive reliance on consistency results in unreliable imputation and alignment without incorporating view-specific cluster information. Thus, we propose an IMVC framework, imputation- and alignment-free for consensus semantics learning (FreeCSL). To bridge semantic gaps across all observations, we learn consensus prototypes from available data to discover a shared space, where semantically similar observations are pulled closer for consensus semantics learning. To capture semantic relationships within specific views, we design a heuristic graph clustering based on modularity to recover cluster structure with intra-cluster compactness and inter-cluster separation for cluster semantics enhancement. Extensive experiments demonstrate, compared to state-of-the-art competitors, FreeCSL achieves more confident and robust assignments on IMVC task.

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Yuzhuo Dai, Jiaqi Jin, Zhibin Dong, Siwei Wang, Xinwang Liu, En Zhu, Xihong Yang, Xinbiao Gan, Yu Feng. 2025-05-16. Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning. https://arxiv.org/abs/2505.11182

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