Unsupervised feature selection via adaptive graph and dependency score

Unsupervised feature selection is an important topic in the fields of machine learning, pattern recognition and data mining. The representation methods include adaptive-graph-based methods and selfrepresentation-based methods. The former methods have a longstanding and undiscovered problem about imbalanced neighbors, and the latter ones do not perform well when features are not linearly dependent. To deal with these problems, a novel unsupervised feature selection method is proposed to ensure k connectivity and eliminate more redundant features based on adaptive graph and dependency score (AGDS). Extensive experiments conducted on 13 benchmark datasets show the effectiveness of AGDS.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

Unsupervised feature selection is an important topic in the fields of machine learning, pattern recognition and data mining. A novel method called AGDS is proposed to address the issues in feature selection by utilizing adaptive graph and dependency score.