Term Selection for a Class of Separable Nonlinear Models - Details

author：

Gan, M. (Gan, M..) ^[1] | Chen, G.-Y. (Chen, G.-Y..) ^[2] | Chen, L. (Chen, L..) ^[3] | Chen, C.L.P. (Chen, C.L.P..) ^[4]

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Scopus

Abstract：

In　this　paper,　we　consider　the　term　selection　problem　for　a　class　of　separable　nonlinear　models.　The　strategy　is　a　two-step　process　in　which　the　nonlinear　parameters　of　the　model　are　first　optimized　by　a　variable　projection　method,　and　then　the　least　absolute　shrinkage　and　selection　operator　are　adopted　to　obtain　a　sparse　solution　by　picking　out　the　critical　terms　automatically.　This　process　may　be　repeated　several　times.　The　proposed　algorithm　is　tested　on　parameter　estimation　problems　for　an　exponential　model　and　a　neural　network-based　model.　The　numerical　results　show　that　the　proposed　algorithm　can　pick　out　the　appropriate　terms　from　the　overparameterized　model　and　the　obtained　parsimonious　model　performs　better　than　other　methods.　©　2012　IEEE.

Keyword：

Least absolute shrinkage and selection operator (LASSO); separable nonlinear models; sparse solution; variable projection (VP)

Community：

[ 1 ] [Gan, M.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China
[ 2 ] [Gan, M.]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, 350116, China
[ 3 ] [Gan, M.]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou, 350003, China
[ 4 ] [Gan, M.]Center for Discrete Mathematics and Theoretical Computer Science, Fuzhou University, Fuzhou, 350116, China
[ 5 ] [Chen, G.-Y.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China
[ 6 ] [Chen, G.-Y.]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, 350116, China
[ 7 ] [Chen, G.-Y.]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou, 350003, China
[ 8 ] [Chen, G.-Y.]Center for Discrete Mathematics and Theoretical Computer Science, Fuzhou University, Fuzhou, 350116, China
[ 9 ] [Chen, L.]Faculty of Science and Technology, University of Macau99999, Macau
[ 10 ] [Chen, C.L.P.]Faculty of Science and Technology, University of Macau99999, Macau
[ 11 ] [Chen, C.L.P.]Department of Computer and Information Science, Faculty of Science and Technology, University of Macau99999, Macau
[ 12 ] [Chen, C.L.P.]Navigation College, Dalian Maritime University, Dalian, 116026, China
[ 13 ] [Chen, C.L.P.]Unmanned System Research Institute, Northwestern Polytechnical University, Xi'an, 710072, China

Reprint 's Address：

[Chen, G.-Y.]College of Mathematics and Computer Science, Fuzhou UniversityChina

Email：

cgykeda@mail.ustc.edu.cn

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Source ：

IEEE Transactions on Neural Networks and Learning Systems

ISSN： 2162-237X

Year： 2020

Issue： 2

Volume： 31

Page： 445-451

1 0 . 4 5 1

JCR@2020

1 0 . 2 0 0

JCR@2023

ESI HC Threshold：149

JCR Journal Grade：1

CAS Journal Grade：1

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WoS CC Cited Count：

SCOPUS Cited Count： 72

ESI Highly Cited Papers on the List： 0 Unfold All

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30 Days PV： 0

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