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Unsupervised feature selection with joint self-expression and spectral analysis via adaptive graph constraints

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Abstract

Unsupervised feature selection (UFS) plays a critical role in the maintenance of representative feature subset from high dimensional data. Both the spectral analysis model and the self-expression model are effective in selecting important features. However, few alternative methods embed these two models into a joint FS framework. To address this problem, we propose a novel UFS method that simultaneously selects the most representative feature subset and makes the selected feature subset discriminative by mapping the original features into the label space. Specifically, both the self-expression and spectral analysis are introduced into our method and the self-expression matrix is used as the FS matrix. The two modules are not simply added together, but interact with each other through two graph constraints which preserve the local structure and the manifold structure of the original data, respectively. Furthermore, this paper proposes an alternative iterative algorithm to solve the four matrices involved in the proposed method. To verify the effectiveness of our method, extensive experiments are implemented, and the experimental results prove that the proposed method achieves the best performance among the current state-of-the-art UFS methods. Moreover, an ablation study is performed to show the effectiveness of each part of the proposed method.

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Acknowledgements

This work was supported in part by the National College Students Innovation and Entrepreneurship Training Program under Grant S202010712042, in part by the Natural Science Foundation of Shaanxi Province under Grant 2020JQ-279, in part by the Doctoral Start-up Foundation of Northwest A&F University under Grant Z1090219095, and Grant Z109021803.

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Authors and Affiliations

  1. College of Information Engineering, Northwest A & F University, Yangling, 712100, Shaanxi, People’s Republic of China

    Mengbo You, Lujie Ban, Yuhan Wang, Juan Kang, Guorui Wang & Aihong Yuan

  2. Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, 712100, Shaanxi, People’s Republic of China

    Mengbo You & Aihong Yuan

  3. Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, 712100, Shaanxi, People’s Republic of China

    Mengbo You & Aihong Yuan

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  1. Mengbo You
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  2. Lujie Ban
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Correspondence to Aihong Yuan.

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You, M., Ban, L., Wang, Y. et al. Unsupervised feature selection with joint self-expression and spectral analysis via adaptive graph constraints. Multimed Tools Appl 82, 5879–5898 (2023). https://doi.org/10.1007/s11042-022-13426-6

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  • DOI: https://doi.org/10.1007/s11042-022-13426-6

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