Unsupervised feature selection via adaptive graph and dependency score

•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.