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Variable Selection In Multivariate Functional Data Classification

Author

Listed:
  • 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 .)

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.

Suggested Citation

  • Górecki Tomasz & Krzyśko Mirosław & ński Waldemar Woły, 2019. "Variable Selection In Multivariate Functional Data Classification," Statistics in Transition New Series, Polish Statistical Association, vol. 20(2), pages 123-138, June.
    • Handle: RePEc:vrs:stintr:v:20:y:2019:i:2:p:123-138:n:3
    DOI: 10.21307/stattrans-2019-018
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    References listed on IDEAS

    as
    1. Ferraty, Frédéric & Vieu, Philippe, 2009. "Additive prediction and boosting for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1400-1413, February.
    2. Jacques, Julien & Preda, Cristian, 2014. "Model-based clustering for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 92-106.
    3. Székely, Gábor J. & Rizzo, Maria L., 2013. "The distance correlation t-test of independence in high dimension," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 193-213.
    4. Székely, Gábor J. & Rizzo, Maria L., 2012. "On the uniqueness of distance covariance," Statistics & Probability Letters, Elsevier, vol. 82(12), pages 2278-2282.
    5. Ferraty, F. & Vieu, P., 2003. "Curves discrimination: a nonparametric functional approach," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 161-173, October.
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