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

doi:10.1016/j.saa.2023.122418

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

	Unsupervised model adaptation for multivariate calibration by domain adaptation-regularization based kernel partial least square
	Shan, Peng 1,5; Bi, Yiming 4; Li, Zhigang 5; Wang, Qiaoyun 5; He, Zhonghai 5; Zhao, Yuhui 3; Peng, Silong 2
刊名	SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
	2023-05-05
卷号	292 页码:17
关键词	Model adaptation Domain-invariant feature representation Projected maximum mean discrepancy Kernel partial least squares
ISSN号	1386-1425
DOI	10.1016/j.saa.2023.122418
通讯作者	Shan, Peng(peng.shan@neuq.edu.cn)
英文摘要	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).
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]
WOS关键词	MAINTENANCE
WOS研究方向	Spectroscopy
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:001009509200001
资助机构	National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Natural Science Foundation of Hebei Province
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53549]
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	1.143 Tai Shan Rd, Qin Huang Dao 066004, 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 4.China Tobacco Zhejiang Ind Co Ltd, Technol Ctr, Hangzhou 310008, Zhejiang, Peoples R China 5.Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Liaoning, 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.