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Relevance-based label distribution feature selection via convex optimization
Qian, Wenbin1,2; Ye, Qianzhi1; Li, Yihui1; Huang, Jintao3; Dai, Shiming2
2022-08-01
Source PublicationInformation Sciences
ISSN0020-0255
Volume607Pages:322-345
Abstract

In label distribution learning, high dimensionality is one of the most prominent characteristics of the data, which increases the model complexity and computational cost. Feature selection is an efficient technique to mitigate the “curse of dimensionality”. The current label distribution feature selection approach based on mutual information employs the heuristic search algorithm to identify the most discriminative features. However, this method can be time-consuming and fall into local optimum. Motivated by this, in this paper, an approach named relevance-based label distribution feature selection via convex optimization is proposed, which takes both feature relevance and label relevance (i.e., label correlation) into account. For features, the relevance to labels calculated by mutual information is considered into a convex optimization function to guide the feature selection process. Compared with the heuristic search, the learning framework for optimization purposes is more conducive to reducing repetitive computations and avoiding the local optimum. For labels, the Pearson correlation coefficient is exploited to describe the correlation information between labels, aiming to enhance the generalization ability of the learning model. Comprehensive experiments are conducted on twenty datasets, and the results demonstrate the effectiveness of the proposed method compared with eight state-of-the-art methods in terms of six evaluation metrics.

KeywordConvex Optimization Feature Relevance Feature Selection Label Correlation Label Distribution Mutual Information
DOI10.1016/j.ins.2022.05.094
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems
WOS IDWOS:000817892200018
Scopus ID2-s2.0-85131946593
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang, 330045, China
2.School of Software, Jiangxi Agricultural University, Nanchang, 330045, China
3.Department of Computer and Information Science, University of Macau, Macau, 999078, China
Recommended Citation
GB/T 7714
Qian, Wenbin,Ye, Qianzhi,Li, Yihui,et al. Relevance-based label distribution feature selection via convex optimization[J]. Information Sciences,2022,607:322-345.
APA Qian, Wenbin,Ye, Qianzhi,Li, Yihui,Huang, Jintao,&Dai, Shiming.(2022).Relevance-based label distribution feature selection via convex optimization.Information Sciences,607,322-345.
MLA Qian, Wenbin,et al."Relevance-based label distribution feature selection via convex optimization".Information Sciences 607(2022):322-345.
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