Feature selection for functional data

Autores
Fraiman, Ricardo; Gimenez, Yanina; Svarc, Marcela
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We herein introduce a general procedure to capture the relevant information from a functional data set in relation to a statistical method used to analyze the data, such as, classification, regression or principal components. The aim is to identify a small subset of functions that can "better explain" the model, highlighting its most important features. We obtain consistency results for our proposals. The computational aspects are analyzed, a heuristic stochastic algorithm is introduced and real data sets are studied.
Fil: Fraiman, Ricardo. Universidad de la República; Uruguay
Fil: Gimenez, Yanina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Departamento de Matemáticas y Ciencias; Argentina
Fil: Svarc, Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Departamento de Matemáticas y Ciencias; Argentina
Materia
CLASSIFICATION
PRINCIPAL COMPONENTS
REGRESSION
VARIABLE SELECTION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/90813
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Feature selection for functional dataFraiman, RicardoGimenez, YaninaSvarc, MarcelaCLASSIFICATIONPRINCIPAL COMPONENTSREGRESSIONVARIABLE SELECTIONhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1We herein introduce a general procedure to capture the relevant information from a functional data set in relation to a statistical method used to analyze the data, such as, classification, regression or principal components. The aim is to identify a small subset of functions that can "better explain" the model, highlighting its most important features. We obtain consistency results for our proposals. The computational aspects are analyzed, a heuristic stochastic algorithm is introduced and real data sets are studied.Fil: Fraiman, Ricardo. Universidad de la República; UruguayFil: Gimenez, Yanina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Departamento de Matemáticas y Ciencias; ArgentinaFil: Svarc, Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Departamento de Matemáticas y Ciencias; ArgentinaElsevier Inc2016-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/zipapplication/pdfhttp://hdl.handle.net/11336/90813Fraiman, Ricardo; Gimenez, Yanina; Svarc, Marcela; Feature selection for functional data; Elsevier Inc; Journal Of Multivariate Analysis; 146; 4-2016; 191-2080047-259XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0047259X15002201info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jmva.2015.09.006info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-12T09:37:51Zoai:ri.conicet.gov.ar:11336/90813instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-11-12 09:37:51.594CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Feature selection for functional data
title Feature selection for functional data
spellingShingle Feature selection for functional data
Fraiman, Ricardo
CLASSIFICATION
PRINCIPAL COMPONENTS
REGRESSION
VARIABLE SELECTION
title_short Feature selection for functional data
title_full Feature selection for functional data
title_fullStr Feature selection for functional data
title_full_unstemmed Feature selection for functional data
title_sort Feature selection for functional data
dc.creator.none.fl_str_mv Fraiman, Ricardo
Gimenez, Yanina
Svarc, Marcela
author Fraiman, Ricardo
author_facet Fraiman, Ricardo
Gimenez, Yanina
Svarc, Marcela
author_role author
author2 Gimenez, Yanina
Svarc, Marcela
author2_role author
author
dc.subject.none.fl_str_mv CLASSIFICATION
PRINCIPAL COMPONENTS
REGRESSION
VARIABLE SELECTION
topic CLASSIFICATION
PRINCIPAL COMPONENTS
REGRESSION
VARIABLE SELECTION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We herein introduce a general procedure to capture the relevant information from a functional data set in relation to a statistical method used to analyze the data, such as, classification, regression or principal components. The aim is to identify a small subset of functions that can "better explain" the model, highlighting its most important features. We obtain consistency results for our proposals. The computational aspects are analyzed, a heuristic stochastic algorithm is introduced and real data sets are studied.
Fil: Fraiman, Ricardo. Universidad de la República; Uruguay
Fil: Gimenez, Yanina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Departamento de Matemáticas y Ciencias; Argentina
Fil: Svarc, Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Departamento de Matemáticas y Ciencias; Argentina
description We herein introduce a general procedure to capture the relevant information from a functional data set in relation to a statistical method used to analyze the data, such as, classification, regression or principal components. The aim is to identify a small subset of functions that can "better explain" the model, highlighting its most important features. We obtain consistency results for our proposals. The computational aspects are analyzed, a heuristic stochastic algorithm is introduced and real data sets are studied.
publishDate 2016
dc.date.none.fl_str_mv 2016-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/90813
Fraiman, Ricardo; Gimenez, Yanina; Svarc, Marcela; Feature selection for functional data; Elsevier Inc; Journal Of Multivariate Analysis; 146; 4-2016; 191-208
0047-259X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/90813
identifier_str_mv Fraiman, Ricardo; Gimenez, Yanina; Svarc, Marcela; Feature selection for functional data; Elsevier Inc; Journal Of Multivariate Analysis; 146; 4-2016; 191-208
0047-259X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0047259X15002201
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jmva.2015.09.006
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/zip
application/pdf
dc.publisher.none.fl_str_mv Elsevier Inc
publisher.none.fl_str_mv Elsevier Inc
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 12.976206

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