Pairwise dependence-based unsupervised feature selection

作者：

Highlights：

• We propose a new unsupervised feature selection based on information theory.

• A single optimization problem is designed considering dependence among features.

• We analyze the convergence of the proposed iterative algorithm.

摘要

•We propose a new unsupervised feature selection based on information theory.•A single optimization problem is designed considering dependence among features.•We analyze the convergence of the proposed iterative algorithm.

论文关键词：Unsupervised feature selection,Feature dependency,Feature redundancy,Joint entropy,l2, 1 regularization

论文评审过程：Received 23 December 2019, Revised 11 August 2020, Accepted 18 September 2020, Available online 19 September 2020, Version of Record 23 September 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107663