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Adaptive graph learning and low-rank constraint for supervised spectral feature selection

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Abstract

Spectral feature selection (SFS) effectively improves performance of feature selection by introducing a graph matrix to preserve information of data. However, conventional SFS (1) generally preserves either global structure or local structure of data in selected subset, which is not capable of providing comprehensive information for model to output a robust result; (2) constructs graph matrix by original data, which usually lead to a suboptimal graph matrix because of redundant information; (3) conducts feature selection task depending on the fixed graph matrix, which is easily trapped in local optimization. Thus, we have proposed a novel SFS to (1) preserve both local information and global information of original data in feature-selected subset to provide comprehensive information for learning model; (2) integrate graph construction and feature selection to propose a robust spectral feature selection easily obtaining global optimization of feature selection. Besides, for the proposed problem, we further provide a optimization algorithm to effectively tackle the problem with a fast convergence. The extensive experimental results showed that our proposed method outperforms state-of-the-art feature selection methods, in terms of classification performance.

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Notes

  1. http://archive.ics.uci.edu/ml/.

  2. http://featureselection.asu.edu/datasets.php.

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Acknowledgements

This work was supported by the program of Research and development of intelligent logistics management system based on Beidou multi-functional information acquisition and monitoring terminal (Grant No: 2016AB04097).

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  1. College of Continuing Education, Guangxi Teachers Education University, Nanning, 530001, Guangxi, People’s Republic of China

    Zhi Zhong

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  1. Zhi Zhong
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Correspondence to Zhi Zhong.

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Zhong, Z. Adaptive graph learning and low-rank constraint for supervised spectral feature selection. Neural Comput & Applic 32, 6503–6512 (2020). https://doi.org/10.1007/s00521-018-04006-7

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  • DOI: https://doi.org/10.1007/s00521-018-04006-7

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