Low-rank dictionary learning for unsupervised feature selection
作者：
Highlights：
• Introduce a joint unsupervised feature selection by a low-rank dictionary learning approach.
• Apply spectral analysis to maintain sample similarities.
• Propose a unified feature selection in a regularized way.
• Experimental results are performed on a variety of applied domains.
• The experimental results reveal the strength of the proposed approach.
摘要
•Introduce a joint unsupervised feature selection by a low-rank dictionary learning approach.•Apply spectral analysis to maintain sample similarities.•Propose a unified feature selection in a regularized way.•Experimental results are performed on a variety of applied domains.•The experimental results reveal the strength of the proposed approach.
论文关键词：Unsupervised feature selection,Dictionary learning,Sparse learning,Spectral analysis,Low-rank representation
论文评审过程：Received 20 June 2021, Revised 15 January 2022, Accepted 29 March 2022, Available online 11 April 2022, Version of Record 6 May 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117149
