中文版 | English
Title

Gradient Learning With the Mode-Induced Loss: Consistency Analysis and Applications

Author
Publication Years
2023
DOI
Source Title
ISSN
2162-2388
EISSN
2162-2388
VolumePPIssue:99Pages:1-14
Abstract
Variable selection methods aim to select the key covariates related to the response variable for learning problems with high-dimensional data. Typical methods of variable selection are formulated in terms of sparse mean regression with a parametric hypothesis class, such as linear functions or additive functions. Despite rapid progress, the existing methods depend heavily on the chosen parametric function class and are incapable of handling variable selection for problems where the data noise is heavy-tailed or skewed. To circumvent these drawbacks, we propose sparse gradient learning with the mode-induced loss (SGLML) for robust model-free (MF) variable selection. The theoretical analysis is established for SGLML on the upper bound of excess risk and the consistency of variable selection, which guarantees its ability for gradient estimation from the lens of gradient risk and informative variable identification under mild conditions. Experimental analysis on the simulated and real data demonstrates the competitive performance of our method over the previous gradient learning (GL) methods.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
Others
Funding Project
National Natural Science Foundation of China["12071166","62122035","61972188"] ; Fundamental Research Funds for the Central Universities of China["2662020LXQD002","2662021JC008"] ; Science and Technology Development Fund, Macau SAR[0049/2022/A1] ; University of Macau[MYRG2022-00072-FST]
WOS Research Area
Computer Science ; Engineering
WOS Subject
Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS Accession No
WOS:000920995400001
Publisher
Data Source
IEEE
PDF urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10021308
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/425405
DepartmentDepartment of Computer Science and Engineering
Affiliation
1.Engineering Research Center of Intelligent Technology for Agriculture, Ministry of Education, Wuhan, China
2.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
3.National Space Science Center, Chinese Academy of Sciences, Beijing, China
4.Department of Computer and Information Science, University of Macau, Macau, China
Recommended Citation
GB/T 7714
Hong Chen,Youcheng Fu,Xue Jiang,et al. Gradient Learning With the Mode-Induced Loss: Consistency Analysis and Applications[J]. IEEE Transactions on Neural Networks and Learning Systems,2023,PP(99):1-14.
APA
Hong Chen.,Youcheng Fu.,Xue Jiang.,Yanhong Chen.,Weifu Li.,...&Feng Zheng.(2023).Gradient Learning With the Mode-Induced Loss: Consistency Analysis and Applications.IEEE Transactions on Neural Networks and Learning Systems,PP(99),1-14.
MLA
Hong Chen,et al."Gradient Learning With the Mode-Induced Loss: Consistency Analysis and Applications".IEEE Transactions on Neural Networks and Learning Systems PP.99(2023):1-14.
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