Using generic order moments for separation of dependent sources with linear conditional expectations

Autores
Caiafa, César Federico; Kuruoglu, Ercan E.
Año de publicación
2013
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In this work, we approach the blind separation of dependent sources based only on a set of their linear mixtures. We prove that, when the sources have a pairwise dependence characterized by the linear conditional expectation (LCE) law, we are able to separate them by maximizing or minimizing a Generic Order Moment (GOM) of their mixture. This general measure includes the higher order as well as the fractional moment cases. Our results, not only confirm some of the existing results for the independent sources case but also they allow us to explore new objective functions for Dependent Component Analysis. A set of examples illustrating the consequences of our theory is presented. Also, a comparison of our GOM based algorithm, the classical FASTICA and a very recently proposed algorithm for dependent sources, the Bounded Component Analysis (BCA) algorithm, is shown.
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
Fil: Kuruoglu, Ercan E.. Istituto di Scienza e Tecnologie dell’Informazione; Italia. Consiglio Nazionale delle Ricerche; Italia
21ª European Signal Processing Conference
Marrakech
Marruecos
European Signal Processing Society (EURASIP)
Materia
Blind Source Separation
Higher Order Moments
Dependent Component Analysis
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:11336/150020
Acceder
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spelling Using generic order moments for separation of dependent sources with linear conditional expectationsCaiafa, César FedericoKuruoglu, Ercan E.Blind Source SeparationHigher Order MomentsDependent Component Analysishttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2In this work, we approach the blind separation of dependent sources based only on a set of their linear mixtures. We prove that, when the sources have a pairwise dependence characterized by the linear conditional expectation (LCE) law, we are able to separate them by maximizing or minimizing a Generic Order Moment (GOM) of their mixture. This general measure includes the higher order as well as the fractional moment cases. Our results, not only confirm some of the existing results for the independent sources case but also they allow us to explore new objective functions for Dependent Component Analysis. A set of examples illustrating the consequences of our theory is presented. Also, a comparison of our GOM based algorithm, the classical FASTICA and a very recently proposed algorithm for dependent sources, the Bounded Component Analysis (BCA) algorithm, is shown.Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Kuruoglu, Ercan E.. Istituto di Scienza e Tecnologie dell’Informazione; Italia. Consiglio Nazionale delle Ricerche; Italia21ª European Signal Processing ConferenceMarrakechMarruecosEuropean Signal Processing Society (EURASIP)European Association For Signal ProcessingGhogho, MounirZoubir, Abdelhak2013info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectConferenciaJournalhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/150020Using generic order moments for separation of dependent sources with linear conditional expectations; 21ª European Signal Processing Conference; Marrakech; Marruecos; 2013; 1-5CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.eurasip.org/Proceedings/Eusipco/Eusipco2013/papers/1569745029.pdfinfo:eu-repo/semantics/altIdentifier/url/https://eurasip.org/Proceedings/Eusipco/Eusipco2013/Internacionalinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:27:54Zoai:ri.conicet.gov.ar:11336/150020instacron: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 15:27:54.762CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Using generic order moments for separation of dependent sources with linear conditional expectations
title Using generic order moments for separation of dependent sources with linear conditional expectations
spellingShingle Using generic order moments for separation of dependent sources with linear conditional expectations
Caiafa, César Federico
Blind Source Separation
Higher Order Moments
Dependent Component Analysis
title_short Using generic order moments for separation of dependent sources with linear conditional expectations
title_full Using generic order moments for separation of dependent sources with linear conditional expectations
title_fullStr Using generic order moments for separation of dependent sources with linear conditional expectations
title_full_unstemmed Using generic order moments for separation of dependent sources with linear conditional expectations
title_sort Using generic order moments for separation of dependent sources with linear conditional expectations
dc.creator.none.fl_str_mv Caiafa, César Federico
Kuruoglu, Ercan E.
author Caiafa, César Federico
author_facet Caiafa, César Federico
Kuruoglu, Ercan E.
author_role author
author2 Kuruoglu, Ercan E.
author2_role author
dc.contributor.none.fl_str_mv Ghogho, Mounir
Zoubir, Abdelhak
dc.subject.none.fl_str_mv Blind Source Separation
Higher Order Moments
Dependent Component Analysis
topic Blind Source Separation
Higher Order Moments
Dependent Component Analysis
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In this work, we approach the blind separation of dependent sources based only on a set of their linear mixtures. We prove that, when the sources have a pairwise dependence characterized by the linear conditional expectation (LCE) law, we are able to separate them by maximizing or minimizing a Generic Order Moment (GOM) of their mixture. This general measure includes the higher order as well as the fractional moment cases. Our results, not only confirm some of the existing results for the independent sources case but also they allow us to explore new objective functions for Dependent Component Analysis. A set of examples illustrating the consequences of our theory is presented. Also, a comparison of our GOM based algorithm, the classical FASTICA and a very recently proposed algorithm for dependent sources, the Bounded Component Analysis (BCA) algorithm, is shown.
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina
Fil: Kuruoglu, Ercan E.. Istituto di Scienza e Tecnologie dell’Informazione; Italia. Consiglio Nazionale delle Ricerche; Italia
21ª European Signal Processing Conference
Marrakech
Marruecos
European Signal Processing Society (EURASIP)
description In this work, we approach the blind separation of dependent sources based only on a set of their linear mixtures. We prove that, when the sources have a pairwise dependence characterized by the linear conditional expectation (LCE) law, we are able to separate them by maximizing or minimizing a Generic Order Moment (GOM) of their mixture. This general measure includes the higher order as well as the fractional moment cases. Our results, not only confirm some of the existing results for the independent sources case but also they allow us to explore new objective functions for Dependent Component Analysis. A set of examples illustrating the consequences of our theory is presented. Also, a comparison of our GOM based algorithm, the classical FASTICA and a very recently proposed algorithm for dependent sources, the Bounded Component Analysis (BCA) algorithm, is shown.
publishDate 2013
dc.date.none.fl_str_mv 2013
dc.type.none.fl_str_mv info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/conferenceObject
Conferencia
Journal
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
status_str publishedVersion
format conferenceObject
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/150020
Using generic order moments for separation of dependent sources with linear conditional expectations; 21ª European Signal Processing Conference; Marrakech; Marruecos; 2013; 1-5
CONICET Digital
CONICET
url http://hdl.handle.net/11336/150020
identifier_str_mv Using generic order moments for separation of dependent sources with linear conditional expectations; 21ª European Signal Processing Conference; Marrakech; Marruecos; 2013; 1-5
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.eurasip.org/Proceedings/Eusipco/Eusipco2013/papers/1569745029.pdf
info:eu-repo/semantics/altIdentifier/url/https://eurasip.org/Proceedings/Eusipco/Eusipco2013/
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/pdf
dc.coverage.none.fl_str_mv Internacional
dc.publisher.none.fl_str_mv European Association For Signal Processing
publisher.none.fl_str_mv European Association For Signal Processing
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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