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Feature selection for functional data

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Apr 2016Embargo end date: 01 Jan 2015 Argentina Publisher:Elsevier BVJournal:Journal of Multivariate Analysis, volume 146, pages 191-208 (issn: 0047-259X,

Authors: Fraiman, Ricardo; Gimenez, Yanina; Svarc, Marcela;

doi: 10.1016/j.jmva.2015.09.006 , 10.48550/arxiv.1502.02123

arXiv: http://arxiv.org/abs/1502.02123

handle: 11336/90813

Feature selection for functional data

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Abstract

In this paper we address the problem of feature selection when the data is functional, we study several statistical procedures including classification, regression and principal components. One advantage of the blinding procedure is that it is very flexible since the features are defined by a set of functions, relevant to the problem being studied, proposed by the user. Our method is consistent under a set of quite general assumptions, and produces good results with the real data examples that we analyze.

22 pages, 4 figures

Country

Argentina

Related Organizations

University of the Republic
Uruguay
University of San Andrés
Argentina
National Scientific and Technical Research Council
Argentina

Keywords

FOS: Computer and information sciences, 62H30, 62H25, 62J05, CLASSIFICATION, PRINCIPAL COMPONENTS, Methodology (stat.ME), VARIABLE SELECTION, REGRESSION, https://purl.org/becyt/ford/1.1, https://purl.org/becyt/ford/1, Statistics - Methodology

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	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	20
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