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Maximum correntropy criterion based regression for multivariate calibration
Peng, Jiangtao1; Guo, Lu1; Hu, Yong2; Rao, KaiFeng3; Xie, Qiwei4,5
Source PublicationCHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
2017-02-15
Volume161Pages:27-33
SubtypeArticle
AbstractThe least-squares criterion is widely used in the multivariate calibration models. Rather than using the conventional linear least-squares metric, we employ a nonlinear correntropy-based metric to describe the spectra-concentrate relations and propose a maximum correntropy criterion based regression (MCCR) model. To solve the correntropy-based model, a half-quadratic optimization technique is developed to convert a non convex and nonlinear optimization problem into an iteratively re-weighted least-squares problem. Finally, MCCR can provide an accurate estimation of the regression relation by alternatively updating an auxiliary vector represented as a nonlinear Gaussian function of fitted residuals and a weight computed by a regularized weighted least-squares model. The proposed method is Compared to some modified PLS algorithms and robust regression methods on four real near-infrared (NIR) spectra data sets. Experimental results demonstrate the efficacy and effectiveness of the proposed method.
KeywordMaximum Correntropy Criterion Least-squares Multivariate Calibration Regularization
WOS HeadingsScience & Technology ; Technology ; Physical Sciences
DOI10.1016/j.chemolab.2016.12.002
WOS KeywordLEAST-SQUARES REGRESSION ; CONTINUUM REGRESSION
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(41501392 ; Natural Science Foundation of Hubei Province(2009CDB387) ; trategic Priority Research Program of the CAS(XDB02060001) ; State Key Joint Laboratory of Environment Simulation and Pollution Control(15K02ESPCR) ; 11371007)
WOS Research AreaAutomation & Control Systems ; Chemistry ; Computer Science ; Instruments & Instrumentation ; Mathematics
WOS SubjectAutomation & Control Systems ; Chemistry, Analytical ; Computer Science, Artificial Intelligence ; Instruments & Instrumentation ; Mathematics, Interdisciplinary Applications ; Statistics & Probability
WOS IDWOS:000394066100004
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14400
Collection类脑智能研究中心
Affiliation1.Hubei Univ, Fac Math & Stat, Hubei Key Lab Appl Math, Wuhan 430062, Peoples R China
2.Beijing Res Inst Uranium Geol, Beijing 100029, Peoples R China
3.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, Beijing 100085, Peoples R China
4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
5.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai Inst Biol Sci, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Peng, Jiangtao,Guo, Lu,Hu, Yong,et al. Maximum correntropy criterion based regression for multivariate calibration[J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS,2017,161:27-33.
APA Peng, Jiangtao,Guo, Lu,Hu, Yong,Rao, KaiFeng,&Xie, Qiwei.(2017).Maximum correntropy criterion based regression for multivariate calibration.CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS,161,27-33.
MLA Peng, Jiangtao,et al."Maximum correntropy criterion based regression for multivariate calibration".CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 161(2017):27-33.
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