Variable selection in multivariate functional data classification

• Authors: Tomasz Górecki , Mirosław Krzyśko , Waldemar Wołyński

Abstracts

EN

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

EN

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

• Publisher: Główny Urząd Statystyczny
• Journal: Statistics in Transition new series
• Year: 2019
• Volume: 20
• Issue: 2
• Pages: 123-138

Contributors

author

Tomasz Górecki

• Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poland

• https://orcid.org/0000000299695257

author

Mirosław Krzyśko

• Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poland

author

Waldemar Wołyński

• Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poland

• https://orcid.org/0000000207779163

Identifiers

DOI

10.21307/stattrans-2019-018

Biblioteka Nauki

1194458