VARIABLE SELECTION IN MULTIVARIATE FUNCTIONAL DATA CLASSIFICATION

Górecki Tomasz (), Krzyśko Mirosław () and ński Waldemar Woły ()
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Górecki Tomasz: Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poznań, Poland .
Krzyśko Mirosław: Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poznań, Poland . Interfaculty Institute of Mathematics and Statistics, The President Stanisław Wojciechowski State University of Applied Sciences in Kalisz, Kalisz, Poland .
ński Waldemar Woły: Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Kalisz, Poland .

Statistics in Transition New Series, 2019, vol. 20, issue 2, 123-138

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. (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:stintr:v:20:y:2019:i:2:p:123-138:n:3

DOI: 10.21307/stattrans-2019-018

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