A nonlinear calibration transfer method based on joint kernel subspace

doi:10.1016/j.chemolab.2021.104247

CORC > 自动化研究所 > 中国科学院自动化研究所 > 智能制造技术与系统研究中心 > 多维数据分析团队

	A nonlinear calibration transfer method based on joint kernel subspace
	Shan, Peng 1; Zhao, Yuhui 3; Wang, Qiaoyun 1; Wang, Shuyu 1; Ying, Yao 1; Peng, Silong 2
刊名	CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
	2021-03-15
卷号	210 页码:14
关键词	Calibration transfer Multivariate calibration Joint kernel subspace Kernel partial least squares (KPLS)
ISSN号	0169-7439
DOI	10.1016/j.chemolab.2021.104247
通讯作者	Shan, Peng(peng.shan@neuq.edu.cn)
英文摘要	A nonlinear strategy is proposed to develop calibration transfer method with the available joint spectral data composed of the master and slave spectra of standard samples. Its core idea is to minimize the instrument-induced spectral variation in the reproducing kernel Hilbert space (RKHS) where the joint spectral data is implicitly mapped by proper kernel function. The nonlinear feature representation of master or slave spectra are reconstructed by singular value decomposition (SVD) in the RKHS. Then the transferred feature of slave spectra approaching the reconstructed feature of master spectra in the RKHS can be acquired by the procedure that (1) calculates the transfer matrix to match the maser and slave kernel features in the joint kernel subspace and (2) utilizes the transferred slave kernel feature to reconstruct the nonlinear feature representation of slave spectra. As a better feature representation suited for multivariate calibration, both the reconstructed feature of master calibration spectra and the transferred feature of slave test spectra are derived in the RKHS. A kernel partial least squares (KPLS) model built on the former is applied to the latter. The KPLS master calibration model is equivalent to a partial least squares (PLS) model built with the corresponding reconstructed master kernel feature in the joint kernel subspace. By exploiting the kernel eigenvector representations and kernel trick, a series of computationally less demanding formulas with linear operation is derived to realize the nonlinear calibration model, termed as joint kernel subspace based calibration transfer (JKSCT). JKSCT is compared with boxcar signal transfer (BST), piecewise direct standardization (PDS), multi-level simultaneous component analysis (MSCA), canonical correlation analysis based calibration transfer (CCACT), generalized least squares (GLS), slope and bias correction (SBC), spectral space transformation (SST), external parameter orthogonalization (EPO) and double competitive adaptive reweighted sampling (DCARS) on three spectral datasets. Experimental results show that JKSCT performs at least comparable with the DCARS or SST, and frequently better than the other methods.
资助项目	National Natural Science Foundation of China[61601104] ; Fundamental Research Funds for the Central Universities[N2023021]
WOS研究方向	Automation & Control Systems ; Chemistry ; Computer Science ; Instruments & Instrumentation ; Mathematics
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000634801200004
资助机构	National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/44158]
专题	自动化研究所_智能制造技术与系统研究中心_多维数据分析团队
通讯作者	Shan, Peng
作者单位	1.Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 3.Northeastern Univ, Sch Comp Sci & Engn, Shenyang 110819, Liaoning, Peoples R China
推荐引用方式 GB/T 7714	Shan, Peng,Zhao, Yuhui,Wang, Qiaoyun,et al. A nonlinear calibration transfer method based on joint kernel subspace[J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS,2021,210:14.
APA	Shan, Peng,Zhao, Yuhui,Wang, Qiaoyun,Wang, Shuyu,Ying, Yao,&Peng, Silong.(2021).A nonlinear calibration transfer method based on joint kernel subspace.CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS,210,14.
MLA	Shan, Peng,et al."A nonlinear calibration transfer method based on joint kernel subspace".CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 210(2021):14.