Semi-supervised multi-label feature selection combining nonlinear manifold structure and minimizing group sparse redundant correlation.
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| Title: | Semi-supervised multi-label feature selection combining nonlinear manifold structure and minimizing group sparse redundant correlation. |
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| Authors: | Li, Runxin1,2 (AUTHOR) rxli@kust.edu.cn, Yang, Xiong2 (AUTHOR) yangx@stu.kust.edu.cn, Li, Xiaowu2 (AUTHOR) lxwlxw66@kust.edu.cn, Shu, Guofeng2 (AUTHOR) shuguofeng@kust.edu.cn, Jia, Lianyin2 (AUTHOR) lianyinjia@kust.edu.cn, Shang, Zhenhong1,2 (AUTHOR) szh@kust.edu.cn |
| Source: | Expert Systems with Applications. May2025, Vol. 273, pN.PAG-N.PAG. 1p. |
| Subjects: | Optimization algorithms, Weight training, Mathematical optimization, Regression analysis, Algorithms, Supervised learning, Feature selection |
| Abstract: | The goal of multi-label feature selection is to identify the most representative features from the original feature grouping so as to effectively mitigate the dimension disaster. Currently, most multi-label feature selection methods based on sparse regression models train the feature weight matrices by directly projecting the original feature space into the label space; however, these direct projection methods may fail to capture the key nonlinear relationships between features and labels. To address this challenge, we propose a novel multi-label feature selection technique named semi-supervised multi-label feature selection combining nonlinear manifold structure and minimizing group sparse redundant correlation (NMS-MGSRC). First, we reconstruct the Hilbert–Schmidt Independence Criterion (HSIC)-based MDDM model (Y. Zhang and Z.-H. Zhou, 2010) into a least-squares problem, allowing the weight matrices to effectively learn the nonlinear correlations between features and labels by generating a nonlinear feature-label manifold structure. Second, we group the feature-label manifold using an adaptive k -means clustering method based on the evaluation of silhouette coefficients, and impose l 1 - and l 2 , 1 -norm constraints on the weight matrices to discriminate group-specific and common features in each feature-label manifold group. Finally, the nonlinear feature-label manifold learning term, group sparse feature learning constraints, semi-supervised learning mechanism, feature-label manifold correlation, and instance correlation learning constraint are integrated into a unified framework that includes two binary l 1 -norm regularity terms, and we introduce a novel optimization algorithm, S-FISTA, to optimize it. Comparative results on 13 multi-label datasets with six evaluation metrics demonstrate that NMS-MGSRC significantly outperforms 13 representative feature selection algorithms. • Remodeling the HSIC-based MDDM model for reconstructing feature-label manifold. • Label-specific and common features of similar manifold groups are learned simultaneously. • A novel bidirectional interacting sparse term is proposed to alleviate the weight over coupling. • The optimization technique S-FISTA is presented to solve the model with two binary l 1 -norm terms. • 13 comparative algorithms under 13 datasets exemplify the superiority of our approach. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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