[1]YAN Fei,WANG Xiaodong.A robust, semi-supervised, and multi-label feature selection method[J].CAAI Transactions on Intelligent Systems,2019,14(4):812-819.[doi:10.11992/tis.201809017]
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A robust, semi-supervised, and multi-label feature selection method

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 4 Page number: 812-819 Column: 学术论文—机器学习 Public date: 2019-07-02
Title:
A robust, semi-supervised, and multi-label feature selection method
Author(s):
YAN Fei;  WANG Xiaodong
College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
Keywords:
feature selection; semi-supervised learning; multi-label learning; l1-norm graph; linear regression; l2; 1-norm; robust; classification; clustering
CLC:
TP391.4
DOI:
10.11992/tis.201809017
Abstract:
The existing semi-supervised multi-label feature selection method constructs a spectral image based on the l2-norm, which is sensitive to noise. To handle this problem, a robust semi-supervised multi-label feature selection method is presented in this study. A global linear regression function is utilized to construct the multi-label feature selection model, and the l1-norm graph is combined to obtain the local discriminant information. Subsequently, the l2,1-norm constraint is added to improve the distinguishability between these features and the stability of regression analysis to avoid noise interference. Four open source datasets are selected to verify the proposed method based on various evaluation criteria. The results demonstrate the efficiency of our method with respect to the classification accuracy and robustness.
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