|Maximum correntropy criterion based regression for multivariate calibration|
|Peng, Jiangtao; Guo, Lu; Hu, Yong; Rao, KaiFeng; Xie, Qiwei
|Source Publication||CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
|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.|
|Keyword||Maximum Correntropy Criterion
Peng, Jiangtao,Guo, Lu,Hu, Yong,et al. Maximum correntropy criterion based regression for multivariate calibration[J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS,2017,161(0):27-33.
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(0),27-33.
Peng, Jiangtao,et al."Maximum correntropy criterion based regression for multivariate calibration".CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 161.0(2017):27-33.
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