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Maximum Correntropy Criterion based Regression for Multivariate Calibration
Mar 31, 2017Author:
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Title: Maximum Correntropy Criterion based Regression for Multivariate Calibration  

Authors: Peng, JT; Guo, L; Hu, Y; Rao, KF; Xie, QW 

Author Full Names: Peng, Jiangtao; Guo, Lu; Hu, Yong; Rao, KaiFeng; Xie, Qiwei 

Source: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 161 27-33; 10.1016/j.chemolab.2016.12.002 FEB 15 2017  

Language: English 

Abstract: The 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. 

ISSN: 0169-7439  

eISSN: 1873-3239  

IDS Number: EK6WG  

Unique ID: WOS:000394066100004 

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