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.2016.12.002FEB 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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Maximum Correntropy Criterion based Regression for Multivariate Calibration
Mar 31, 2017Author：