Variable selection in multivariate functional data classification

Tomasz Górecki; Mirosław Krzyśko; Waldemar Wołyński

doi:10.21307/stattrans-2019-018

Variable selection in multivariate functional data classification

Tomasz Górecki Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poland ORCID:https://orcid.org/0000-0002-9969-5257 , Mirosław Krzyśko Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poland. Interfaculty Institute of Mathematics and Statistics, The President Stanisław Wojciechowski State University of Applied Sciences in Kalisz, Poland ORCID:https://orcid.org/0000-0001-0075-4432 , Waldemar Wołyński Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poland ORCID:https://orcid.org/0000-0002-0777-9163 Statistics in Transition new series, vol. 20, 2019, 2, pages: 123-138 Published online: 2 July 2019 DOI 10.21307/stattrans-2019-018

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ARTICLE

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ABSTRACT

A new variable selection method is considered in the setting of classification with multivariate functional data (Ramsay and Silverman (2005)). The variable selection is a dimensionality reduction method which leads to replace the whole vector process, with a low-dimensional vector still giving a comparable classification error. Various classifiers appropriate for functional data are used. The proposed variable selection method is based on functional distance covariance (dCov) given by Székely and Rizzo (2009, 2012) and the Hilbert-Schmidt Independent Criterion (HSIC) given by Gretton et al. (2005). This method is a modification of the procedure given by Kong et al. (2015). The proposed methodology is illustrated with a real data example.

KEYWORDS

multivariate functional data, variable selection, dCov, HSIC, classification

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