CASIA OpenIR
Iterative Reweighted Quantile Regression Using Augmented Lagrangian Optimization for Baseline Correction
Han QJ(韩权杰)1,2; Peng SL(彭思龙)1,2; Xie Q(谢琼)1,2; Wu YF(吴义凡)1,2; Zhang GW(张根伟)3
2018
Conference Name2018 5th International Conference on Information Science and Control Engineering
Conference Date2018年7月20至22
Conference Place河南郑州
Abstract

Based on baseline is a smooth curve and under
the collected spectrum, a robust penalized quantile regression
with B-spline basis has been proposed to baseline estimation.
Then an iterative reweighted method has been adopted for
quantile regression optimization. Instead of man tuning the
hyperparameter in penalized quantile regression, augmented
Lagrangian method is applied to hyperparameter optimization.
Experiments on simulated and real data sets show that our
method is more effective in baseline correction than other
baseline estimation methods in simulated data set. For real
data set, the calibration results after the baseline correction
step are better than other preprocessing and baseline correction
methods.

Indexed ByEI
Funding ProjectNational Natural Science Foundation of China[61571438]
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23501
Collection中国科学院自动化研究所
Corresponding AuthorPeng SL(彭思龙)
Affiliation1.中国科学院自动化研究所
2.中国科学院大学
3.北京药物化学研究所
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Han QJ,Peng SL,Xie Q,et al. Iterative Reweighted Quantile Regression Using Augmented Lagrangian Optimization for Baseline Correction[C],2018.
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