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Label distribution feature selection based on label-specific features

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Abstract

Label distribution learning, where deal with label ambiguity by describing the degree of relevance of each label to a specific instance. As a novel machine learning paradigm, the curse of dimensionality is one of the prominent problems. Feature selection is a vital preprocessing step to reduce the high dimensionality of data. However, most existing label distribution feature selection methods focus on selecting a feature subset that has relevant capabilities for all labels, ignoring label-specific features with the maximum discriminatory power for each label. To tackle this issue, a label distribution feature selection algorithm based on label-specific features is proposed in this paper. Initially, we introduce a feature selection optimization model for label distribution data that simultaneously considers common and label-specific features, leveraging sparse learning to further investigate the intricate relationships between features and labels. Subsequently, the label correlation coefficient is employed to enhance the collaborative learning effect of labels. Finally, the relevance between features and labels is taken into account to guide the feature selection process, which can effectively eliminate the redundant features. Comprehensive experiments demonstrate the advantage of our proposed method over other well-established feature selection algorithms for selecting label-specific features to label distribution data.

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Data sharing not applicable.

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Code availability not applicable.

Notes

  1. http://palm.seu.edu.cn/xgeng/LDL/index.htm

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Acknowledgements

This work is supported by National Natural Science Foundation of China (62266018 and 62366019), and Natural Science Foundation of Jiangxi Province (20202BABL202037 and 20232

BAB202052).

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Authors and Affiliations

  1. School of Information Engineering, East China Jiaotong University, Nanchang, China

    Wenhao Shu & Qiang Xia

  2. School of Software, Jiangxi Agricultural University, Nanchang, China

    Wenbin Qian

Authors
  1. Wenhao Shu
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  2. Qiang Xia
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  3. Wenbin Qian
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Contributions

Wenhao Shu: Conceptualization, Methodology, Visualization, Writing-original draft. Qiang Xia: Data curation, Software, Validation, Formal analysis, Writing-original draft. Wenbin Qian: Investigation, Supervision, Writing-review and editing.

Corresponding author

Correspondence to Wenbin Qian.

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Shu, W., Xia, Q. & Qian, W. Label distribution feature selection based on label-specific features. Appl Intell 54, 9195–9212 (2024). https://doi.org/10.1007/s10489-024-05668-8

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