Incomplete Multi-view Clustering via Hierarchical Semantic Alignment and Cooperative Completion

• URL: http://arxiv.org/abs/2510.13887v3
• Date: Mon, 27 Oct 2025 03:20:35 GMT
• Title: Incomplete Multi-view Clustering via Hierarchical Semantic Alignment and Cooperative Completion
• Authors: Xiaojian Ding, Lin Zhao, Xian Li, Xiaoying Zhu,
• Abstract summary: This paper proposes a novel incomplete multi-view clustering framework based on Hierarchical Semantic Alignment and Cooperative Completion (HSACC)<n>HSACC achieves robust cross-view fusion through a dual-level semantic space design.<n> Experimental results demonstrate that HSACC significantly outperforms state-of-the-art methods on five benchmark datasets.
• Score: 13.39263294343983
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Incomplete multi-view data, where certain views are entirely missing for some samples, poses significant challenges for traditional multi-view clustering methods. Existing deep incomplete multi-view clustering approaches often rely on static fusion strategies or two-stage pipelines, leading to suboptimal fusion results and error propagation issues. To address these limitations, this paper proposes a novel incomplete multi-view clustering framework based on Hierarchical Semantic Alignment and Cooperative Completion (HSACC). HSACC achieves robust cross-view fusion through a dual-level semantic space design. In the low-level semantic space, consistency alignment is ensured by maximizing mutual information across views. In the high-level semantic space, adaptive view weights are dynamically assigned based on the distributional affinity between individual views and an initial fused representation, followed by weighted fusion to generate a unified global representation. Additionally, HSACC implicitly recovers missing views by projecting aligned latent representations into high-dimensional semantic spaces and jointly optimizes reconstruction and clustering objectives, enabling cooperative learning of completion and clustering. Experimental results demonstrate that HSACC significantly outperforms state-of-the-art methods on five benchmark datasets. Ablation studies validate the effectiveness of the hierarchical alignment and dynamic weighting mechanisms, while parameter analysis confirms the model's robustness to hyperparameter variations.

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