Unsupervised feature selection via adaptive graph and dependency score

作者：

Highlights：

• A novel unsupervised feature selection method based on adaptive graph learning and dependency score (AGDS) is proposed.

• AGDS can make each sample adaptively learn from its k nearest neighbors so that the problem about imbalanced neighbors and trivial solution can be prevented.

• AGDS introduce dependency score by incorporating mutual information and entropy to measure feature uncertainty and feature pairwise dependency so that more redundant features can be eliminated.

• Extensive experiments conducted on 13 benchmark datasets show the effectiveness of AGDS.

摘要

•A novel unsupervised feature selection method based on adaptive graph learning and dependency score (AGDS) is proposed.•AGDS can make each sample adaptively learn from its k nearest neighbors so that the problem about imbalanced neighbors and trivial solution can be prevented.•AGDS introduce dependency score by incorporating mutual information and entropy to measure feature uncertainty and feature pairwise dependency so that more redundant features can be eliminated.•Extensive experiments conducted on 13 benchmark datasets show the effectiveness of AGDS.

论文关键词：Unsupervised feature selection,Adaptive graph,Mutual information,Entropy

论文评审过程：Received 28 September 2021, Revised 8 February 2022, Accepted 3 March 2022, Available online 10 March 2022, Version of Record 16 March 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108622