Incomplete Multi-view Clustering via Local Reasoning and Correlation Analysis
Authors: 
Xiaocui Li, Guoliang Li, Xinyu Zhang, Yangtao Wang, Qingyu Shi, Wei LiangAuthors Info & Claims
Pages 484 - 492
Abstract
In recent years, incomplete multi-view clustering (IMVC) has attracted considerable attention for its ability to acheieve effective clustering results through the integration of key information amidst missing view. However, the existing IMVC methods are still faced with 3 limitations: (1) They exhibit deficiencies in considering the weight distribution within views, (2) they ignore the varying contributions of different views to the common consistent representation, and (3) they struggle to sufficiently extract and recover the vital information within incomplete views. To address these limitations, we incorporates local reasoning and correlation analysis to design an incomplete multi-view clustering method(IMVCLRCA), which introduces a new strategy of feature learning and missing view recovery, fully exploiting local similarity and structural continuity within views and performing precise local reasoning recovery on missing data. By maximizing mutual information between views through contrastive learning, we achieve the consistent representation learning of multiple views. Furthermore, based on semantic consistency, we comprehensively consider the correlation between views, utilized a weight matrix to fuse cross-view data, and constructed a view with a correlation structure, ultimately obtaining a common consistent representation. We conduct extensive experiments on 4 public datasets including Caltech101-20, BBCSport, Scene-15, and LandUse-21. Experimental results demonstrate that IMVCLRCA has higher accuracy and robustness compared to the state-of-the-art IMVC methods. The anonymous code of this project is available on GitHub at https://github.com/ggg2111/2025WSDM-IMVCLRCA.
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Index Terms
- Incomplete Multi-view Clustering via Local Reasoning and Correlation Analysis
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Published In
March 2025
1151 pages
ISBN:9798400713293
DOI:10.1145/3701551
- General Chairs:
- Wolfgang Nejdl,
- Sören Auer,
- Proceedings Chair:
- Oliver Karras,
- Program Chairs:
- Meeyoung Cha,
- Marie-Francine Moens,
- Marc Najork
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Published: 10 March 2025
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- Research-article
Funding Sources
- the Guangdong Provincial Department of Education Innovation Strong School Youth Innovation Talent Project
- the Natural Science Foundation of Hunan Province
- the Ministry of Education in China Project of Humanities and Social Sciences
- the National Key Research and Development Program of China
- the National Natural Science Foundation of China
- the Open Project of Xiangjiang Laboratory
- the Guangdong Basic and Applied Basic Research Foundation
- the Guangzhou Science and Technology Planning Project
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WSDM '25: The Eighteenth ACM International Conference on Web Search and Data Mining
March 10 - 14, 2025
Hannover, Germany
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Overall Acceptance Rate 498 of 2,863 submissions, 17%
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