Unsupervised model adaptation for multivariate calibration by domain adaptation-regularization based kernel partial least square
文献类型：期刊论文
| 作者 | Shan, Peng2,5; Bi, Yiming4; Li, Zhigang5; Wang, Qiaoyun5; He, Zhonghai5; Zhao, Yuhui1; Peng, Silong3
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| 刊名 | SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
 |
| 出版日期 | 2023-05-05 |
| 卷号 | 292页码:17 |
| ISSN号 | 1386-1425 |
| 关键词 | Model adaptation Domain-invariant feature representation Projected maximum mean discrepancy Kernel partial least squares |
| 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). |
| WOS关键词 | MAINTENANCE |
| 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 |
| 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.Northeastern Univ, Sch Comp Sci & Engn, Shenyang 110819, Liaoning, Peoples R China 2.143 Tai Shan Rd, Qin Huang Dao 066004, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, 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. |
入库方式： OAI收割
来源：自动化研究所
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