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...

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Bibliographic Details
Published in:Information sciences Vol. 480; pp. 144 - 159
Main Authors: Wang, Hongjun, Zhang, Yinghui, Zhang, Ji, Li, Tianrui, Peng, Lingxi
Format: Journal Article
Language:English
Published: Elsevier Inc 01.04.2019
Subjects:
Factor graph
Feature selection
Message-passing algorithm
Unsupervised learning
Feature selection
Factor graph
Message-passing algorithm
Unsupervised learning
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary:•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. In this paper, a factor graph model for unsupervised feature selection (FGUFS) is proposed. FGUFS explicitly measures the similarities between features; these similarities are passed to each other as messages in the graph model. The importance score of each feature is calculated using the message-passing algorithm, and then feature selection is performed based on the final importance scores. Extensive experiments were performed on several datasets, and the results demonstrate that FGUFS outperforms other state-of-art unsupervised feature selection algorithms on several performance measures.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2018.12.034