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Multi-label feature selection based on minimizing feature redundancy of mutual information.

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Title: Multi-label feature selection based on minimizing feature redundancy of mutual information.
Authors: Zhou, Gaozhi1 (AUTHOR) gz_zhou@stu.kust.edu.cn, Li, Runxin1,2 (AUTHOR) rxli@kust.edu.cn, Shang, Zhenhong1 (AUTHOR) szh@kust.edu.cn, Li, Xiaowu1 (AUTHOR) lxwlxw66@kust.edu.cn, Jia, Lianyin1 (AUTHOR) lianyinjia@kust.edu.cn
Source: Neurocomputing. Nov2024, Vol. 607, pN.PAG-N.PAG. 1p.
Subjects: Model theory
Abstract: Multi-label feature selection is an indispensable technology in the preprocessing of multi-label high-dimensional data. Approaches utilizing information theory and sparse models hold promise in this domain, demonstrating strong performance. Although there have been extensive literatures using l 1 and l 2 , 1 -norms to identify label-specific features and common features in the feature space, they all ignore the redundant information interference problem when different features are learned simultaneously. Considering that features and labels in multi-label data are rarely linearly correlated, the MFS-MFR approach is presented to generate a representation of the nonlinear correlation between features and labels using the mutual information estimator. Following that, MFS-MFR detects specific and common features in the feature-label mutual information space using two coefficient matrices constrained by the l 1 and l 2 , 1 -norms, respectively. In particular, we define a non-zero correlation constraint that effectively minimizes the redundant correlation between the two matrices. Moreover, a manifold regularization term is devised to preserve the local information of the mutual information space. To solve the optimization model with nonlinear binary regular term, we employ a novel solution approach called S-FISTA. Extensive experiments across 15 multi-label benchmark datasets, comparing against 11 top-performing multi-label feature selection methods, demonstrate the superior performance of MFS-MFR. [ABSTRACT FROM AUTHOR]
Copyright of Neurocomputing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 179499502
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Multi-label feature selection is an indispensable technology in the preprocessing of multi-label high-dimensional data. Approaches utilizing information theory and sparse models hold promise in this domain, demonstrating strong performance. Although there have been extensive literatures using l 1 and l 2 , 1 -norms to identify label-specific features and common features in the feature space, they all ignore the redundant information interference problem when different features are learned simultaneously. Considering that features and labels in multi-label data are rarely linearly correlated, the MFS-MFR approach is presented to generate a representation of the nonlinear correlation between features and labels using the mutual information estimator. Following that, MFS-MFR detects specific and common features in the feature-label mutual information space using two coefficient matrices constrained by the l 1 and l 2 , 1 -norms, respectively. In particular, we define a non-zero correlation constraint that effectively minimizes the redundant correlation between the two matrices. Moreover, a manifold regularization term is devised to preserve the local information of the mutual information space. To solve the optimization model with nonlinear binary regular term, we employ a novel solution approach called S-FISTA. Extensive experiments across 15 multi-label benchmark datasets, comparing against 11 top-performing multi-label feature selection methods, demonstrate the superior performance of MFS-MFR. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Neurocomputing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1016/j.neucom.2024.128392
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      – Code: eng
        Text: English
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      – TitleFull: Multi-label feature selection based on minimizing feature redundancy of mutual information.
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              Text: Nov2024
              Type: published
              Y: 2024
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              Value: 607
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