Unsupervised model adaptation for multivariate calibration by domain adaptation-regularization based kernel partial least square

doi:10.1016/j.saa.2023.122418

CASIA OpenIR > 智能制造技术与系统研究中心 > 多维数据分析（彭思龙）-技术团队

	Unsupervised model adaptation for multivariate calibration by domain adaptation-regularization based kernel partial least square
	Shan, Peng 1,5; Bi, Yiming 2; Li, Zhigang 1; Wang, Qiaoyun 1; He, Zhonghai 1; Zhao, Yuhui 3; Peng, Silong4
发表期刊	SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
ISSN	1386-1425
	2023-05-05
卷号	292 页码:17
通讯作者	Shan, Peng(peng.shan@neuq.edu.cn)
摘要	In chemometrics, calibration model adaptation is desired when training-and test-samples come from different distributions. Domain-invariant feature representation is currently a successful strategy to realize model adap-tation and has received wide attention. The paper presents a nonlinear unsupervised model adaptation method termed as domain adaption regularization-based kernel partial least squares regression (DarKPLS). DarKPLS aims to minimize the source and target distributions in a low-dimensional latent space projected from the reproducing kernel Hilbert space (RKHS) generated with the labeled source data and unlabeled target data. Specially, the distributional means and variances between source and target latent variables are aligned in the RKHS. By extending existing domain invariant partial least square regression (di-PLS) with the projected maximum mean discrepancy (PMMD) to reduce the mean discrepancy in the RKHS further, DarKPLS could realize fine-grained domain alignment that further improves the adaptation performance. DarKPLS is applied to the gamma-poly-glutamic acid fermentation dataset, tobacco dataset and corn dataset, and it demonstrates improved prediction results in comparison with No adaptation partial least squares (PLS), null augmented regression (NAR), extended linear joint trained framework (ExtJT), scatter component analysis (SCA) and domain-invariant iterative partial least squares (DIPALS).
关键词	Model adaptation Domain-invariant feature representation Projected maximum mean discrepancy Kernel partial least squares
DOI	10.1016/j.saa.2023.122418
关键词[WOS]	MAINTENANCE
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61601104] ; Fundamental Research Funds for the Central Universities[N2023021] ; Natural Science Foundation of Hebei Province[F2017501052]
项目资助者	National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Natural Science Foundation of Hebei Province
WOS研究方向	Spectroscopy
WOS类目	Spectroscopy
WOS记录号	WOS:001009509200001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/53550
专题	智能制造技术与系统研究中心_多维数据分析（彭思龙）-技术团队
通讯作者	Shan, Peng
作者单位	1.Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China 2.China Tobacco Zhejiang Ind Co Ltd, Technol Ctr, Hangzhou 310008, Zhejiang, Peoples R China 3.Northeastern Univ, Sch Comp Sci & Engn, Shenyang 110819, Liaoning, Peoples R China 4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 5.143 Tai Shan Rd, Qin Huang Dao 066004, Peoples R China
推荐引用方式 GB/T 7714	Shan, Peng,Bi, Yiming,Li, Zhigang,et al. Unsupervised model adaptation for multivariate calibration by domain adaptation-regularization based kernel partial least square[J]. SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY,2023,292:17.
APA	Shan, Peng.,Bi, Yiming.,Li, Zhigang.,Wang, Qiaoyun.,He, Zhonghai.,...&Peng, Silong.(2023).Unsupervised model adaptation for multivariate calibration by domain adaptation-regularization based kernel partial least square.SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY,292,17.
MLA	Shan, Peng,et al."Unsupervised model adaptation for multivariate calibration by domain adaptation-regularization based kernel partial least square".SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 292(2023):17.