期刊
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
卷 167, 期 -, 页码 196-207出版社
ELSEVIER
DOI: 10.1016/j.chemolab.2017.06.007
关键词
Nonlinear models; Cross-validatory framework; Optimal parameter estimation; Kernel parameter; Number of latent variable sets; Combined objective function
类别
资金
- Fundamental Research Funds for Central Universities [JUSRP51510]
- National Natural Science Foundation of China [61273070]
- 111 project [12018]
- Jiangsu College Graduate Research Innovation Project [KYLX15-1168]
This article revisits recently proposed methods to determine the kernel parameter and the number of latent components for identifying kernel principal component analysis (KPCA) and kernel partial least squares (KPLS) models. A detailed analysis shows that existing work is neither optimal nor efficient in determining these important parameters and may lead to erroneous estimates. In addition to that, most methods are not designed to simultaneously estimate both parameters, i.e. they require one parameter to be predetermined. To address these practically important issues, the article introduces a cross-validatory framework to optimally determine both parameters. Application studies to a simulation example and a total of three experimental or industrial data sets confirm that the cross-validatory framework outperforms existing methods and yields optimal estimations for both parameters. In sharp contrast, existing work has the potential to substantially overestimate the number of latent components and to provide inadequate estimates for the kernel parameter.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据