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Feature selection via uncorrelated discriminant sparse regression for multimedia analysis

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Abstract

As an important part of multimedia analysis applications, feature selection has attracted much attention during the past decades. Lots of feature selection methods have been proposed, but most of them neglect to consider the correlation between the selected features, which leads to the feature redundancy problem. In this paper, we propose a novel supervised feature selection method, termed as Uncorrelated Discriminant Sparse Regression (UDSR). This method is an organic combination of discriminant sparse regression and uncorrelated constraint. In this method, the discriminant sparse regression ensures the discriminant power of the selected features, and the uncorrelated constraint avoids the redundancy of selected features. Thus the features selected by our method are not only discriminative but also uncorrelated with each other. The method can be applied to a wide range of multimedia applications. Experiments are conducted on two video datasets and four image datasets. The experimental results show that the proposed method has better performance for multimedia analysis, compared to the baseline and six state-of-the-art relative methods.

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Acknowledgements

This work was supported in part by the Chinese Natural Science Foundation (CNSF) (under Grant 61702165). This work was supported in part by the S&T Program of Hebei, China (under Grant F2020111001). This work was supported in part by the Scientific Research Project of Hengshui University (under Grant 2021GC17, Grant 2021yj18). This work was supported in part by the Humanities and Social Sciences project of the Ministry of Education(under Grant 18YJCZH129).

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Authors and Affiliations

  1. The College of Mathematics and Computer Science, Hengshui University, Hengshui, China

    Jianguang Zhang

  2. The School of Information Engineering, Binzhou University, Binzhou, China

    Shuangle Guo

  3. The School of Computer Science & Software Engineering, Shenzhen University, Shenzhen, China

    Jianguang Zhang

  4. The College Electronics & Information Engineering, Hengshui University, Hengshui, China

    Wenting Zhang

  5. The Office of Academic Research, Hengshui University, Hengshui, China

    Zhifei Song

  6. The College of Continuing Education, Hengshui University, Hengshui, China

    Chunmei Meng

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  1. Shuangle Guo
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Correspondence to Jianguang Zhang.

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Guo, S., Zhang, J., Zhang, W. et al. Feature selection via uncorrelated discriminant sparse regression for multimedia analysis. Multimed Tools Appl 82, 619–647 (2023). https://doi.org/10.1007/s11042-022-13258-4

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