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Gaussian process regression with functional covariates and multivariate response

Wang, B, Chen, Tao and Xu, A (2017) Gaussian process regression with functional covariates and multivariate response Chemometrics and Intelligent Laboratory Systems, 163 (April). pp. 1-6.

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Abstract

Gaussian process regression (GPR) has been shown to be a powerful and effective non- parametric 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 multidimen- sional. The model naturally incorporates two different types of covariates: multivari- ate 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 correla- tions 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.

Item Type: Article
Subjects : Chemical & Process Engineering
Divisions : Faculty of Engineering and Physical Sciences > Chemical and Process Engineering
Authors :
NameEmailORCID
Wang, BUNSPECIFIEDUNSPECIFIED
Chen, TaoT.Chen@surrey.ac.ukUNSPECIFIED
Xu, AUNSPECIFIEDUNSPECIFIED
Date : 3 February 2017
Identification Number : 10.1016/j.chemolab.2017.02.001
Copyright Disclaimer : © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords : Gaussian process regression, functional data analysis, functional covariates, multivariate response, semi-metrics
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 10 Feb 2017 08:39
Last Modified : 07 Jul 2017 13:30
URI: http://epubs.surrey.ac.uk/id/eprint/813503

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