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Incomplete multi-view clustering with incomplete graph-regularized orthogonal non-negative matrix factorization

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Abstract

Incomplete multi-view clustering (IMC) has achieved widespread attention due to its advantage in fusing the multi-view information when the view samples are unobserved partly. Recently, it is shown that the clustering performance in the subspace can be improved by preserving the clustering structure of each view, but the problem of the inconsistent clustering structure caused by the incomplete graphs are seldom considered, restricting the clustering performance. Motivated by the clustering interpretation of the orthogonal non-negative matrix factorization, it is employed to unify the clustering structure of the data, and a new model called Incomplete Graph-regularized Orthogonal Non-negative Matrix Factorization (IGONMF) is proposed in this paper. In IGONMF, the reproduced representation is developed, based on which, a set of incomplete graphs are utilized to fully take advantage of the geometric structure of the data. And the orthogonality is further employed to alleviate the problem of the inconsistent clustering structure. Also, we design an effective iterative updating algorithm to solve the proposed model, along with its analysis on the convergence and the computational cost. Finally, experimental results on several real-world datasets indicate that our method is superior to the related state-of-the-art methods.

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Notes

  1. http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-11/www/wwkb/index.html

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

  3. https://github.com/youweiliang/ConsistentGraphLearning/tree/master/data

  4. http://www.lamda.nju.edu.cn/Data.ashx

  5. https://sites.google.com/view/jerry-wen-hit/publications

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Authors and Affiliations

  1. School of Automation, Guangdong Key Laboratory of IoT Information Technology, Guangdong University of Technology, Guangzhou, 510006, China

    Naiyao Liang & Zuyuan Yang

  2. Key Laboratory of iDetection and Manufacturing-IoT, Ministry of Education, Guangzhou, 510006, China

    Zhenni Li

  3. School of Electrical Engineering, Guangzhou Railway Polytechnic, Guangzhou, 510430, Guangdong, China

    Wei Han

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Correspondence to Naiyao Liang.

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This article belongs to the Topical Collection: Special Issue on Multi-view Learning

Guest Editors: Guoqing Chao, Xingquan Zhu, Weiping Ding, Jinbo Bi and Shiliang Sun

This work was supported in part by the Key-Area Research and Development Program of Guangdong Province under Grants 2019B010154002, 2019B010118001, and 2019B010121001; in part by the National Natural Science Foundation of China under Grants 61803096, 61801133, and U191140003; in part by the Guangzhou Science and Technology Program Project under Grant 202002030289; in part by the Guangdong Natural Science Foundation under Grant 2022A1515010688.

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Liang, N., Yang, Z., Li, Z. et al. Incomplete multi-view clustering with incomplete graph-regularized orthogonal non-negative matrix factorization. Appl Intell 52, 14607–14623 (2022). https://doi.org/10.1007/s10489-022-03551-y

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