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Robust sparse manifold discriminant analysis

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Abstract

Classical linear discriminant analysis (LDA) has been applied to machine learning and pattern recognition successfully, and many variants based on LDA are proposed. However, the traditional LDA has several disadvantages as follows: Firstly, since the features selected by feature selection have good interpretability, LDA has poor performance in feature selection. Secondly, there are many redundant features or noisy data in the original data, but LDA has poor robustness to noisy data and outliers. Lastly, LDA only utilizes the global discriminant information, without consideration for the local discriminant structure. In order to overcome the above problems, we present a robust sparse manifold discriminant analysis (RSMDA) method. In RSMDA, by introducing the L2,1 norm, the most discriminant features can be selected for discriminant analysis. Meanwhile, the local manifold structure is used to capture the local discriminant information of the original data. Due to the introduction of L2,1 constraints and local discriminant information, the proposed method has excellent robustness to noisy data and has the potential to perform better than other methods. A large number of experiments on different data sets have proved the good effectiveness of RSMDA.

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Acknowledgements

This work was supported by Natural Science Foundation of China (U1504610, 61971339, 61471161), the Key Project of the Natural Science Foundation of Shanxi Province (2018JZ6002), Scientific and Technological Innovation Team of Colleges and Universities in Henan Province (20IRTSTHN018), the Doctoral Startup Foundation of Xi’an Polytechnic University (BS1616). the Natural Science Foundations of Henan Province (202300410148).

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Authors and Affiliations

  1. Information Engineering College, Henan University of Science and Technology, Luoyang, China

    Jingjing Wang, Zhonghua Liu, Qingtao Wu & Mingchuan Zhang

  2. College of Electronics and Information, Xi’an Polytechnic University, Xi’an, China

    Kaibing Zhang

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  1. Jingjing Wang
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  2. Zhonghua Liu
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  3. Kaibing Zhang
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  4. Qingtao Wu
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Correspondence to Zhonghua Liu.

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Wang, J., Liu, Z., Zhang, K. et al. Robust sparse manifold discriminant analysis. Multimed Tools Appl 81, 20781–20796 (2022). https://doi.org/10.1007/s11042-022-12708-3

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