Unsupervised feature selection via transformed auto-encoder

作者：

Highlights：

• Propose to select features by auto-encoder with non-negativity and orthogonality.

• Construct a lifted transformed net that can rank original features.

• Provide a new perspective for feature selection with efficient embedding property.

摘要

•Propose to select features by auto-encoder with non-negativity and orthogonality.•Construct a lifted transformed net that can rank original features.•Provide a new perspective for feature selection with efficient embedding property.

论文关键词：Machine learning,Deep learning,Feature selection,Unsupervised learning,Auto-encoder

论文评审过程：Received 17 August 2020, Revised 1 November 2020, Accepted 1 January 2021, Available online 9 January 2021, Version of Record 18 January 2021.

论文官网地址：https://doi.org/10.1016/j.knosys.2021.106748