Kernel reconstruction learning
Wu, Yun1,2; Xiong, Shifeng2
Source PublicationNEUROCOMPUTING
AbstractThis paper proposes a class of kernel interpolation-based methods, called kernel reconstruction learning, for solving machine learning problems. Kernel reconstruction learning uses kernel interpolators to recon-struct the unknown functions, which are needed to estimate in the problem, with estimated function val-ues at selected knots. It can be applied to any learning problem that involves function estimation. We prove a reconstruction representer theorem, which indicates that conventional kernel methods, including kernel ridge regression, kernel support vector machine, and kernel logistic regression, can be viewed as special cases of kernel reconstruction learning. Furthermore, kernel reconstruction learning provides new algorithms for large datasets. The kernel reconstruction vector machine, kernel reconstruction logistic regression, and kernel reconstruction density estimation are discussed in detail. With appropriate imple-mentations, they are shown to have higher prediction/estimation accuracy and/or less computational cost than popular kernel methods.(c) 2022 Elsevier B.V. All rights reserved.
KeywordKernel method Representer theorem Interpolation Classification Density estimation Sequential algorithm
Indexed BySCI
Funding ProjectNational Natural Science Foundation of China ; [12171462]
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000904832400001
Citation statistics
Document Type期刊论文
Corresponding AuthorXiong, Shifeng
Affiliation1.Univ Chinese Acad Sci, Sch Math Sci, Beijing, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, NCMIS, KLSC, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Wu, Yun,Xiong, Shifeng. Kernel reconstruction learning[J]. NEUROCOMPUTING,2023,522:1-10.
APA Wu, Yun,&Xiong, Shifeng.(2023).Kernel reconstruction learning.NEUROCOMPUTING,522,1-10.
MLA Wu, Yun,et al."Kernel reconstruction learning".NEUROCOMPUTING 522(2023):1-10.
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