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Semi-Supervised Multi-Label Dimensionality Reduction Learning by Instance and Label Correlations

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  • Runxin Li

    (Yunnan Key Lab of Computer Technology Applications, Kunming University of Science and Technology, Kunming 650500, China
    Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
    These authors contributed equally to this work.)

  • Jiaxing Du

    (Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
    These authors contributed equally to this work.)

  • Jiaman Ding

    (Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China)

  • Lianyin Jia

    (Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China)

  • Yinong Chen

    (School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85287, USA)

  • Zhenhong Shang

    (Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China)

Abstract

The label learning mechanism is challenging to integrate into the training model of the multi-label feature space dimensionality reduction problem, making the current multi-label dimensionality reduction methods primarily supervision modes. Many methods only focus attention on label correlations and ignore the instance interrelations between the original feature space and low dimensional space. Additionally, very few techniques consider how to constrain the projection matrix to identify specific and common features in the feature space. In this paper, we propose a new approach of semi-supervised multi-label dimensionality reduction learning by instance and label correlations (SMDR-IC, in short). Firstly, we reformulate MDDM which incorporates label correlations as a least-squares problem so that the label propagation mechanism can be effectively embedded into the model. Secondly, we investigate instance correlations using the k -nearest neighbor technique, and then present the l 1 -norm and l 2 , 1 -norm regularization terms to identify the specific and common features of the feature space. Experiments on the massive public multi-label data sets show that SMDR-IC has better performance than other related multi-label dimensionality reduction methods.

Suggested Citation

  • Runxin Li & Jiaxing Du & Jiaman Ding & Lianyin Jia & Yinong Chen & Zhenhong Shang, 2023. "Semi-Supervised Multi-Label Dimensionality Reduction Learning by Instance and Label Correlations," Mathematics, MDPI, vol. 11(3), pages 1-25, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:782-:d:1057188
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    References listed on IDEAS

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    2. Hubert, Mia & Van Driessen, Katrien, 2004. "Fast and robust discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 301-320, March.
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