A factor graph model for unsupervised feature selection.

•A novel filter type unsupervised feature selection algorithm, namely, a factor graph model for unsupervised feature selection (FGUFS) is proposed.•In FGUFS, the maximal information coefficient (MIC) is used to measure the similarities between features, and a message passing algorithm developed for the purpose is used to infer the factor graph.•Extensive experiments show the strengths of FGUFS over existing methods to achieve high clustering accuracy, RI and purity while containing few redundant features.