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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| Published in: | Information sciences Vol. 480; pp. 144 - 159 |
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| Language: | English |
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01.04.2019
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| ISSN: | 0020-0255, 1872-6291 |
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| Abstract | •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. |
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| AbstractList | •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. |
| Author | Zhang, Yinghui Li, Tianrui Zhang, Ji Peng, Lingxi Wang, Hongjun |
| Author_xml | – sequence: 1 givenname: Hongjun surname: Wang fullname: Wang, Hongjun email: wanghongjun@swjtu.edu.cn organization: School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China – sequence: 2 givenname: Yinghui surname: Zhang fullname: Zhang, Yinghui organization: Software Center, Northeastern University, Shenyang 110819, China – sequence: 3 givenname: Ji surname: Zhang fullname: Zhang, Ji email: JiZhang@my.swjtu.edu.cn organization: School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China – sequence: 4 givenname: Tianrui orcidid: 0000-0003-3550-3495 surname: Li fullname: Li, Tianrui email: trli@swjtu.edu.cn organization: School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China – sequence: 5 givenname: Lingxi surname: Peng fullname: Peng, Lingxi email: scu.peng@gmail.com organization: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, Guangdong 510006, China |
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