Probabilistic matching pursuit with Gabor dictionaries

Autores
Ferrando, Sebastian Esteban; Doolittle, E.J.; Bernal, A. J.; Bernal, Luis
Año de publicación
2000
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We propose a probabilistic extension of the matching pursuit adaptive signal processing algorithm introduced by Mallat and others. In adaptive signal processing, signals are expanded in terms of a large linearly dependent `dictionary' of functions rather than in terms of an orthonormal basis. Matching pursuit is a simple greedy algorithm for generating an expansion of a given signal. In probabilistic matching pursuit multiple random expansions are obtained as estimates for a given signal. The new algorithm is illustrated in the context of signal denoising. Although most of the random expansions generated by probabilistic matching pursuit are poorer estimates for the signal than those obtained by matching pursuit, our final estimate, obtained as an expected value computed by means of an ergodic average, can improve the result obtained by MP in some denoising situations. One of the major underlying ideas is a novel notion of coherence between a signal and the dictionary. Several simulated examples are presented.
Fil: Ferrando, Sebastian Esteban. Ryerson Polytechnic University; Canadá
Fil: Doolittle, E.J.. University of Toronto; Canadá
Fil: Bernal, A. J.. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
Fil: Bernal, Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
Materia
Matching Pursuit
Gabor Function Dictionary
Denoising
Rejection Sampling
Bernoulli Shift
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:11336/39206
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/39206
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Probabilistic matching pursuit with Gabor dictionariesFerrando, Sebastian EstebanDoolittle, E.J.Bernal, A. J.Bernal, LuisMatching PursuitGabor Function DictionaryDenoisingRejection SamplingBernoulli Shifthttps://purl.org/becyt/ford/3.1https://purl.org/becyt/ford/3We propose a probabilistic extension of the matching pursuit adaptive signal processing algorithm introduced by Mallat and others. In adaptive signal processing, signals are expanded in terms of a large linearly dependent `dictionary' of functions rather than in terms of an orthonormal basis. Matching pursuit is a simple greedy algorithm for generating an expansion of a given signal. In probabilistic matching pursuit multiple random expansions are obtained as estimates for a given signal. The new algorithm is illustrated in the context of signal denoising. Although most of the random expansions generated by probabilistic matching pursuit are poorer estimates for the signal than those obtained by matching pursuit, our final estimate, obtained as an expected value computed by means of an ergodic average, can improve the result obtained by MP in some denoising situations. One of the major underlying ideas is a novel notion of coherence between a signal and the dictionary. Several simulated examples are presented.Fil: Ferrando, Sebastian Esteban. Ryerson Polytechnic University; CanadáFil: Doolittle, E.J.. University of Toronto; CanadáFil: Bernal, A. J.. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Departamento de Física; ArgentinaFil: Bernal, Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Departamento de Física; ArgentinaElsevier Science2000-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/39206Ferrando, Sebastian Esteban; Doolittle, E.J.; Bernal, A. J.; Bernal, Luis; Probabilistic matching pursuit with Gabor dictionaries; Elsevier Science; Signal Processing; 80; 10; 12-2000; 2099-21200165-1684CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/S0165-1684(00)00071-2info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0165168400000712info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:57:51Zoai:ri.conicet.gov.ar:11336/39206instacron: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:34982025-09-03 09:57:52.109CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Probabilistic matching pursuit with Gabor dictionaries
title Probabilistic matching pursuit with Gabor dictionaries
spellingShingle Probabilistic matching pursuit with Gabor dictionaries
Ferrando, Sebastian Esteban
Matching Pursuit
Gabor Function Dictionary
Denoising
Rejection Sampling
Bernoulli Shift
title_short Probabilistic matching pursuit with Gabor dictionaries
title_full Probabilistic matching pursuit with Gabor dictionaries
title_fullStr Probabilistic matching pursuit with Gabor dictionaries
title_full_unstemmed Probabilistic matching pursuit with Gabor dictionaries
title_sort Probabilistic matching pursuit with Gabor dictionaries
dc.creator.none.fl_str_mv Ferrando, Sebastian Esteban
Doolittle, E.J.
Bernal, A. J.
Bernal, Luis
author Ferrando, Sebastian Esteban
author_facet Ferrando, Sebastian Esteban
Doolittle, E.J.
Bernal, A. J.
Bernal, Luis
author_role author
author2 Doolittle, E.J.
Bernal, A. J.
Bernal, Luis
author2_role author
author
author
dc.subject.none.fl_str_mv Matching Pursuit
Gabor Function Dictionary
Denoising
Rejection Sampling
Bernoulli Shift
topic Matching Pursuit
Gabor Function Dictionary
Denoising
Rejection Sampling
Bernoulli Shift
purl_subject.fl_str_mv https://purl.org/becyt/ford/3.1
https://purl.org/becyt/ford/3
dc.description.none.fl_txt_mv We propose a probabilistic extension of the matching pursuit adaptive signal processing algorithm introduced by Mallat and others. In adaptive signal processing, signals are expanded in terms of a large linearly dependent `dictionary' of functions rather than in terms of an orthonormal basis. Matching pursuit is a simple greedy algorithm for generating an expansion of a given signal. In probabilistic matching pursuit multiple random expansions are obtained as estimates for a given signal. The new algorithm is illustrated in the context of signal denoising. Although most of the random expansions generated by probabilistic matching pursuit are poorer estimates for the signal than those obtained by matching pursuit, our final estimate, obtained as an expected value computed by means of an ergodic average, can improve the result obtained by MP in some denoising situations. One of the major underlying ideas is a novel notion of coherence between a signal and the dictionary. Several simulated examples are presented.
Fil: Ferrando, Sebastian Esteban. Ryerson Polytechnic University; Canadá
Fil: Doolittle, E.J.. University of Toronto; Canadá
Fil: Bernal, A. J.. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
Fil: Bernal, Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Mar del Plata. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
description We propose a probabilistic extension of the matching pursuit adaptive signal processing algorithm introduced by Mallat and others. In adaptive signal processing, signals are expanded in terms of a large linearly dependent `dictionary' of functions rather than in terms of an orthonormal basis. Matching pursuit is a simple greedy algorithm for generating an expansion of a given signal. In probabilistic matching pursuit multiple random expansions are obtained as estimates for a given signal. The new algorithm is illustrated in the context of signal denoising. Although most of the random expansions generated by probabilistic matching pursuit are poorer estimates for the signal than those obtained by matching pursuit, our final estimate, obtained as an expected value computed by means of an ergodic average, can improve the result obtained by MP in some denoising situations. One of the major underlying ideas is a novel notion of coherence between a signal and the dictionary. Several simulated examples are presented.
publishDate 2000
dc.date.none.fl_str_mv 2000-12
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/11336/39206
Ferrando, Sebastian Esteban; Doolittle, E.J.; Bernal, A. J.; Bernal, Luis; Probabilistic matching pursuit with Gabor dictionaries; Elsevier Science; Signal Processing; 80; 10; 12-2000; 2099-2120
0165-1684
CONICET Digital
CONICET
url http://hdl.handle.net/11336/39206
identifier_str_mv Ferrando, Sebastian Esteban; Doolittle, E.J.; Bernal, A. J.; Bernal, Luis; Probabilistic matching pursuit with Gabor dictionaries; Elsevier Science; Signal Processing; 80; 10; 12-2000; 2099-2120
0165-1684
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/S0165-1684(00)00071-2
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0165168400000712
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.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv 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
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score 13.13397

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