Abstract
Multi-view clustering (MVC) has significantly been developed with the advances in information acquisition technologies. Most of MVC methods benefit from complete observation of all views so that consistent information and complementary information contained in different views can be extracted effectively. However, not all views of instances are always available in practical applications. In addition, existing self-representation learning methods for subspace clustering do not investigate the local structure of incomplete views to their full extent. This paper introduces a novel approach called Locality Adaptive Incomplete Multi-view Subspace Clustering (LAIMSC) to address the aforementioned challenges. Unlike previous incomplete multi-view subspace clustering that the process of local learning and clustering is often separated into two steps, our LAIMSC method seamlessly integrates local learning and subspace clustering into a unified framework, which can effectively capture complementary information from different incomplete views. In the meantime, due to the incompleteness of views, partitions of different views might be pretty different. Consequently, the proposed LAIMSC method generates an optimal partition for each view and then aligns each partition to form a consensus partition. The LAIMSC method demonstrates superior performance compared to current incomplete multi-view clustering algorithms, as validated by extensive experiments conducted on a variety of challenging datasets.
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Acknowledgements
This work was supported in part by the Humanities and Social Sciences Research Project of the Ministry of Education of China under Grant 23YJC910012 (Research on Clustering Methods with Automatic Completion for Incomplete Multi-view Data), in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2024A1515011729, Grant 2023A1515012534, Grant 2023A1515012718, and Grant 2023A1515011344, in part by the Guangdong Province Ordinary University Characteristic Innovation Project under Grant 2023KTSCX031, in part by the Special Basic Cooperative Research Programs of Yunnan Provincial Undergraduate Universities under Grant 202301BA070001-095, and in part by the Guangdong Province Philosophy and Social Science Planning Co-construction Project under Grant GD23XZY07.
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Guo Zhong, Min Zhong, Shengqi Wu, and Pengfei Song wrote the main manuscript text, and Yuzhi Liang prepared formal analysis and visualization. Yuzhi Liang, Shixun Lin, and Xiuyun Zhu reviewed the manuscript.
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Zhong, G., Zhong, M., Wu, S. et al. Locality adaptive incomplete multi-view subspace clustering. Data Min Knowl Disc 39, 36 (2025). https://doi.org/10.1007/s10618-025-01109-3
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DOI: https://doi.org/10.1007/s10618-025-01109-3