Cross-validatory framework for optimal parameter estimation of KPCA and KPLS models

Autor: Lei Xie, David Rooney, Jillian M. Thompson, Zhe Li, Yujia Fu, Uwe Kruger, Juergen Hahn, Huizhong Yang

Publikováno v: Fu, Y, Kruger, U, Li, Z, Xie, L, Thompson, J, Rooney, D, Hahn, J & Yang, H 2017, ' Cross-validatory framework for optimal parameter estimation of KPCA and KPLS models ', Chemometrics and Intelligent Laboratory Systems, vol. 167, pp. 196-207 . https://doi.org/10.1016/j.chemolab.2017.06.007

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

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https://pure.qub.ac.uk/en/publications/crossvalidatory-framework-for-optimal-parameter-estimation-of-kpca-and-kpls-models(7e6cb413-d933-42d8-b162-3c8429df01b3).html