A factor graph model for unsupervised feature selection

• Impact Factor:8.233
• DOI number:10.1016/j.ins.2018.12.034
• Affiliation of Author(s):西南交通大学
• Journal:Information Sciences
• Place of Publication:UNITED STATES
• Key Words:Feature selection Factor graph Message-passing algorithm Unsupervised learning
• Abstract: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.
• Co-author:Yinghui Zhang, Ji Zhang,Tianrui Li, Lingxi Peng
• First Author:Hongjun Wang
• Indexed by:Academic papers
• Correspondence Author:Hongjun Wang
• Document Code:20185206297318
• Discipline:Engineering
• First-Level Discipline:Computer Science and Technology
• Volume:Volume 480,
• Issue:April 2019,
• Page Number:Pages 144-159
• ISSN No.:0020-0255
• Translation or Not:no
• Date of Publication:2018-12-21