Please use this identifier to cite or link to this item:
Title: Gaussian process regression with functional covariates and multivariate response
Authors: Wang, Bo
Chen, Tao
Xu, Aiping
First Published: 3-Feb-2017
Publisher: Elsevier
Citation: Chemometrics and Intelligent Laboratory Systems, 2017, 163, pp. 1–6
Abstract: Gaussian process regression (GPR) has been shown to be a powerful and effective nonparametric method for regression, classification and interpolation, due to many of its desirable properties. However, most GPR models consider univariate or multivariate covariates only. In this paper we extend the GPR models to cases where the covariates include both functional and multivariate variables and the response is multidimensional. The model naturally incorporates two different types of covariates: multivariate and functional, and the principal component analysis is used to de-correlate the multivariate response which avoids the widely recognised difficulty in the multi-output GPR models of formulating covariance functions which have to describe the correlations not only between data points but also between responses. The usefulness of the proposed method is demonstrated through a simulated example and two real data sets in chemometrics.
DOI Link: 10.1016/j.chemolab.2017.02.001
ISSN: 0169-7439
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © Elsevier, 2017. This article is distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License ( ), which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Description: The file associated with this record is embargoed until 12 months after the date of publication. The final published version may be available through the links above. Following the embargo period the above license applies.
Appears in Collections:Published Articles, Dept. of Mathematics

Files in This Item:
File Description SizeFormat 
gp_fun_reg_multi_scalar_resp_r.pdfPost-review (final submitted author manuscript)333.8 kBAdobe PDFView/Open

Items in LRA are protected by copyright, with all rights reserved, unless otherwise indicated.