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Gaussian process regression with functional covariates and multivariate response
Responsible organisation
2017 (English)In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 163, no Supplement C, p. 1-6Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2017. Vol. 163, no Supplement C, p. 1-6
Keywords [en]
Gaussian process regression, Functional data analysis, Functional covariates, Multivariate response, Semi-metrics
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:polar:diva-3869DOI: 10.1016/j.chemolab.2017.02.001OAI: oai:DiVA.org:polar-3869DiVA, id: diva2:1161938
Available from: 2017-12-01 Created: 2017-12-01 Last updated: 2017-12-01

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Publisher's full texthttp://www.sciencedirect.com/science/article/pii/S0169743917300059
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