- Autor
- Górecki Tomasz (Adam Mickiewicz University in Poznań, Poland), Krzyśko Mirosław (Adam Mickiewicz University in Poznań, Poland), Wołyński Waldemar (Adam Mickiewicz University in Poznań, Poland)
- Tytuł
- Variable Selection in Multivariate Functional Data Classification
- Źródło
- Statistics in Transition, 2019, vol. 20, nr 2, s. 123-138, rys., tab., bibliogr. s. 136-138
- Słowa kluczowe
- Wielowymiarowa analiza statystyczna, Analiza danych funkcjonalnych, Klasyfikacja
Multi-dimensional statistical analysis, Functional data analysis, Classification - Uwagi
- summ.
- Abstrakt
- 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. (original abstract)
- Dostępne w
- Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka SGH im. Profesora Andrzeja Grodka
Biblioteka Główna Uniwersytetu Ekonomicznego w Katowicach - Pełny tekst
- Pokaż
- Bibliografia
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- Cytowane przez
- ISSN
- 1234-7655
- Język
- eng
- URI / DOI
- http://dx.doi.org/10.21307/stattrans-2019-018