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. |
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| 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] |
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| Database: | Engineering Source |
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