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Incomplete multi-view clustering based on low-rank representation with adaptive graph regularization

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Abstract

Incomplete multi-view clustering has attracted attention due to its ability to deal with clustering problems with incomplete information. However, most existing methods either ignore the local structure of the data or fail to consider the importance of different views. In addition, some methods based on mean filling may easily introduce useless information when the data has a large missing rate. To address these issues, this paper proposes an incomplete multi-view clustering algorithm based on graph regularized low-rank representations without using filling method. Specifically, we combine a distance regularization term and low-rank representation-based non-negativity constraints to directly learn graphs with global and local data structures from raw data. Furthermore, we introduce a novel weighted fusion mechanism in the model to learn a consistent representation of all views, which effectively avoids bad views from affecting the quality of the final fused consensus graph. Experimental results on six incomplete multi-view datasets demonstrate that our proposed method achieves the best performance compared with the existing state-of-the-art methods.

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Data availability

Data openly available in a public repository.

Notes

  1. https://archive.ics.uci.edu/ml/datasets/One-hundred+plant+species+leaves+data+set.

  2. http://mlg.ucd.ie/datasets/3sources.html.

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Funding

This work was supported in part by the National Natural Science Foundation of China (No.61866033), Gansu Provincial Department of Education University Teachers Innovation Fund Project (No.2023B-056), the Introduction of Talent Research Project of Northwest Minzu University (No. xbmuyjrc201904), the Fundamental Research Funds for the Central Universities (No.31920220019, 31920220130), the Gansu Provincial First-class Discipline Program of Northwest Minzu University (No.11080305), the Leading Talent of National Ethnic Affairs Commission (NEAC), the Young Talent of NEAC, and the Innovative Research Team of NEAC (2018) 98.

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Authors and Affiliations

  1. Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Chinese National Information Technology Research Institute, Northwest Minzu University, Lanzhou, 730030, Gansu, China

    Kaiwu Zhang & Shiqiang Du

  2. College of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, 730030, Gansu, China

    Baokai Liu, Shiqiang Du, Yao Yu & **mei Song

Authors
  1. Kaiwu Zhang
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  2. Baokai Liu
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  3. Shiqiang Du
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  4. Yao Yu
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  5. **mei Song
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Contributions

KZ contributed to conceptualization, software, and writing. BL contributed to data curation, writing, and original draft preparation. SD contributed to methodology, visualization, validation, formal analysis. YY contributed to investigation and validation. JS contributed to review and editing.

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Correspondence to Shiqiang Du.

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Zhang, K., Liu, B., Du, S. et al. Incomplete multi-view clustering based on low-rank representation with adaptive graph regularization. Soft Comput 27, 7131–7146 (2023). https://doi.org/10.1007/s00500-023-07919-1

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