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[2510.13887] Incomplete Multi-view Clustering via Hierarchical Semantic Alignment and Cooperative Completion
Summary
This paper presents a novel framework for incomplete multi-view clustering using Hierarchical Semantic Alignment and Cooperative Completion (HSACC), addressing limitations in existing methods by enhancing cross-view fusion and recovering missing data.
Why It Matters
Incomplete multi-view data presents significant challenges in machine learning, particularly in clustering tasks. This research introduces a robust framework that improves upon traditional methods, potentially leading to more accurate data analysis and insights across various applications in AI and data science.
Key Takeaways
- HSACC framework enhances clustering by addressing incomplete multi-view data.
- Utilizes dual-level semantic space for improved cross-view fusion.
- Dynamically assigns view weights based on distributional affinity.
- Demonstrates superior performance on five benchmark datasets.
- Includes code availability for further research and application.
Electrical Engineering and Systems Science > Image and Video Processing arXiv:2510.13887 (eess) [Submitted on 14 Oct 2025 (v1), last revised 20 Feb 2026 (this version, v4)] Title:Incomplete Multi-view Clustering via Hierarchical Semantic Alignment and Cooperative Completion Authors:Xiaojian Ding, Lin Zhao, Xian Li, Xiaoying Zhu View a PDF of the paper titled Incomplete Multi-view Clustering via Hierarchical Semantic Alignment and Cooperative Completion, by Xiaojian Ding and 3 other authors View PDF HTML (experimental) 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 r...