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Incomplete multi-view clustering via local and global co-regularization

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Abstract

The incompleteness of multi-view data is a phenomenon associated with real-world data mining applications, which brings a huge challenge for multi-view clustering. Although various types of clustering methods, which try to obtain a complete and consensus clustering result from a latent subspace, have been developed to overcome this problem, most methods excessively rely on views-public instances to bridge the connection with view-private instances. When lacking sufficient views-public instances, existing methods fail to transmit the information among incomplete views effectively. To overcome this limitation, we propose an incomplete multi-view clustering algorithm via local and global co-regularization (IMVC-LG). In this algorithm, we define a new objective function that is composed of two terms: local clustering from each view and global clustering from multiple views, which constrain each other to exploit the local clustering information from different incomplete views and determine a global consensus clustering result, respectively. Furthermore, an iterative optimization method is proposed to minimize the objective function. Finally, we compare the proposed algorithm with other state-of-the-art incomplete multi-view clustering methods on several benchmark datasets to illustrate its effectiveness.

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Acknowledgements

This work was supported by National Key Research and Development Program of China (Grant No. 2020AAA0106100) and National Natural Science Foundation of China (Grant Nos. 62022052, 62006147).

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Authors and Affiliations

  1. School of Computer and Information Technology, Shanxi University, Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Taiyuan, 030006, China

    Jiye Liang, Xiaolin Liu, Liang Bai & Fuyuan Cao

  2. Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China

    Dianhui Wang

  3. Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, VIC, 3086, Australia

    Dianhui Wang

Authors
  1. Jiye Liang
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  2. Xiaolin Liu
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  3. Liang Bai
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  4. Fuyuan Cao
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  5. Dianhui Wang
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Correspondence to Jiye Liang.

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Liang, J., Liu, X., Bai, L. et al. Incomplete multi-view clustering via local and global co-regularization. Sci. China Inf. Sci. 65, 152105 (2022). https://doi.org/10.1007/s11432-020-3369-8

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  • DOI: https://doi.org/10.1007/s11432-020-3369-8

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