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Semi-supervised multi-view clustering by label relaxation based non-negative matrix factorization

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Abstract

Semi-supervised multi-view clustering in the subspace has attracted sustained attention. The existing methods often project the samples with the same label into the same point in the low dimensional space. This hard constraint-based method magnifies the dimension reduction error, restricting the subsequent clustering performance. To relax the labeled data during projection, we propose a novel method called label relaxation-based semi-supervised non-negative matrix factorization (LRSNMF). In our method, we first employ the Spearman correlation coefficient to measure the similarity between samples. Based on this, we design a new relaxed non-negative label matrix for better subspace learning, instead of the binary matrix. Also, we derive an updated algorithm based on an alternative iteration rule to solve the proposed model. Finally, the experimental results on three real-world datasets (i.e., MSRC, ORL1, and ORL2) with six evaluation indexes (i.e., accuracy, NMI, purity, F-score, precision, and recall) show the advantages of our LRSNMF, with comparison to the state-of-the-art methods.

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Acknowledgements

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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Authors and Affiliations

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

    Zuyuan Yang, Huimin Zhang, Naiyao Liang, Zhenni Li & Weijun Sun

  2. The Ante Laser Co., Ltd, Guangzhou, 510006, China

    Zuyuan Yang

  3. Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing, Guangzhou, 510006, China

    Weijun Sun

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  1. Zuyuan Yang
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Yang, Z., Zhang, H., Liang, N. et al. Semi-supervised multi-view clustering by label relaxation based non-negative matrix factorization. Vis Comput 39, 1409–1422 (2023). https://doi.org/10.1007/s00371-022-02419-z

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