Learning optimal smooth invariant subspaces for data approximation

Autores
Barbieri, Davide; Cabrelli, Carlos; Hernández, Eugenio; Molter, Ursula Maria
Año de publicación
2024
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this article, we consider the problem of approximating a finite set of data (usually huge in applications) by invariant subspaces generated by a small set of smooth functions. The invariance is either by translations under a full-rank lattice or through the action of crystallographic groups. Smoothness is ensured by stipulating that the generators belong to a Paley-Wiener space, which is selected in an optimal way based on the characteristics of the given data. To complete our investigation, we analyze the fundamental role played by the lattice in the process of approximation.
Fil: Barbieri, Davide. Universidad Autónoma de Madrid; España
Fil: Cabrelli, Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina
Fil: Hernández, Eugenio. Universidad Autónoma de Madrid; España
Fil: Molter, Ursula Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina
Materia
INVARIANT SUBSPACES
DATA APPROXIMATION
PALEY-WIENER SPACES
OPTIMAL SUBSPACES
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/234790
Acceder
id CONICETDig_d15a4cff45a5efd8099437cbe89686ce
oai_identifier_str oai:ri.conicet.gov.ar:11336/234790
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Learning optimal smooth invariant subspaces for data approximationBarbieri, DavideCabrelli, CarlosHernández, EugenioMolter, Ursula MariaINVARIANT SUBSPACESDATA APPROXIMATIONPALEY-WIENER SPACESOPTIMAL SUBSPACEShttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1In this article, we consider the problem of approximating a finite set of data (usually huge in applications) by invariant subspaces generated by a small set of smooth functions. The invariance is either by translations under a full-rank lattice or through the action of crystallographic groups. Smoothness is ensured by stipulating that the generators belong to a Paley-Wiener space, which is selected in an optimal way based on the characteristics of the given data. To complete our investigation, we analyze the fundamental role played by the lattice in the process of approximation.Fil: Barbieri, Davide. Universidad Autónoma de Madrid; EspañaFil: Cabrelli, Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; ArgentinaFil: Hernández, Eugenio. Universidad Autónoma de Madrid; EspañaFil: Molter, Ursula Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; ArgentinaAcademic Press Inc Elsevier Science2024-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/234790Barbieri, Davide; Cabrelli, Carlos; Hernández, Eugenio; Molter, Ursula Maria; Learning optimal smooth invariant subspaces for data approximation; Academic Press Inc Elsevier Science; Journal of Mathematical Analysis and Applications; 538; 2; 3-2024; 1-200022-247XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.jmaa.2024.128348info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0022247X24002701?via%3Dihubinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:47:55Zoai:ri.conicet.gov.ar:11336/234790instacron: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:34982025-10-15 14:47:55.356CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Learning optimal smooth invariant subspaces for data approximation
title Learning optimal smooth invariant subspaces for data approximation
spellingShingle Learning optimal smooth invariant subspaces for data approximation
Barbieri, Davide
INVARIANT SUBSPACES
DATA APPROXIMATION
PALEY-WIENER SPACES
OPTIMAL SUBSPACES
title_short Learning optimal smooth invariant subspaces for data approximation
title_full Learning optimal smooth invariant subspaces for data approximation
title_fullStr Learning optimal smooth invariant subspaces for data approximation
title_full_unstemmed Learning optimal smooth invariant subspaces for data approximation
title_sort Learning optimal smooth invariant subspaces for data approximation
dc.creator.none.fl_str_mv Barbieri, Davide
Cabrelli, Carlos
Hernández, Eugenio
Molter, Ursula Maria
author Barbieri, Davide
author_facet Barbieri, Davide
Cabrelli, Carlos
Hernández, Eugenio
Molter, Ursula Maria
author_role author
author2 Cabrelli, Carlos
Hernández, Eugenio
Molter, Ursula Maria
author2_role author
author
author
dc.subject.none.fl_str_mv INVARIANT SUBSPACES
DATA APPROXIMATION
PALEY-WIENER SPACES
OPTIMAL SUBSPACES
topic INVARIANT SUBSPACES
DATA APPROXIMATION
PALEY-WIENER SPACES
OPTIMAL SUBSPACES
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this article, we consider the problem of approximating a finite set of data (usually huge in applications) by invariant subspaces generated by a small set of smooth functions. The invariance is either by translations under a full-rank lattice or through the action of crystallographic groups. Smoothness is ensured by stipulating that the generators belong to a Paley-Wiener space, which is selected in an optimal way based on the characteristics of the given data. To complete our investigation, we analyze the fundamental role played by the lattice in the process of approximation.
Fil: Barbieri, Davide. Universidad Autónoma de Madrid; España
Fil: Cabrelli, Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina
Fil: Hernández, Eugenio. Universidad Autónoma de Madrid; España
Fil: Molter, Ursula Maria. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina
description In this article, we consider the problem of approximating a finite set of data (usually huge in applications) by invariant subspaces generated by a small set of smooth functions. The invariance is either by translations under a full-rank lattice or through the action of crystallographic groups. Smoothness is ensured by stipulating that the generators belong to a Paley-Wiener space, which is selected in an optimal way based on the characteristics of the given data. To complete our investigation, we analyze the fundamental role played by the lattice in the process of approximation.
publishDate 2024
dc.date.none.fl_str_mv 2024-03
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/11336/234790
Barbieri, Davide; Cabrelli, Carlos; Hernández, Eugenio; Molter, Ursula Maria; Learning optimal smooth invariant subspaces for data approximation; Academic Press Inc Elsevier Science; Journal of Mathematical Analysis and Applications; 538; 2; 3-2024; 1-20
0022-247X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/234790
identifier_str_mv Barbieri, Davide; Cabrelli, Carlos; Hernández, Eugenio; Molter, Ursula Maria; Learning optimal smooth invariant subspaces for data approximation; Academic Press Inc Elsevier Science; Journal of Mathematical Analysis and Applications; 538; 2; 3-2024; 1-20
0022-247X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jmaa.2024.128348
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0022247X24002701?via%3Dihub
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Academic Press Inc Elsevier Science
publisher.none.fl_str_mv Academic Press Inc Elsevier Science
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
_version_ 1846082997328019456
score 13.22299

Publicaciones similares