A fast gradient approximation for nonlinear blind signal processing

Autores
Caiafa, Cesar Federico; Sole-Casals, Jordi
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
When dealing with nonlinear blind processing algorithms (deconvolution or post-nonlinear source separation) complex mathematical estimations must be done giving as a result very slow algorithms. This is the case, for example, in speech processing, spike signals deconvolution or microarray data analysis. In this paper, we propose a simple method to reduce computational time for the inversion of Wiener systems or the separation of post-nonlinear mixtures, by using a linear approximation in a minimum-mutual information algorithm. Simulation results demonstrate that linear spline interpolation is fast and accurate, obtaining very good results (similar to those obtained without approximation) while computational time is dramatically decreased. On the other hand, cubic spline interpolation also obtains similar good results, but due to its intrinsically complexity the global algorithm is much more slow and hence not useful for our purpose.
Fil: Caiafa, Cesar Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - Conicet - la Plata. Instituto Argentino de Radioastronomia (i); Argentina
Fil: Sole-Casals, Jordi. Universidad de Vic; España
Materia
Blind Deconvolution
Blind Source Separation
Minimum Mutual Information Methods
Wiener Systems
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:11336/4091
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/4091
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A fast gradient approximation for nonlinear blind signal processingCaiafa, Cesar FedericoSole-Casals, JordiBlind DeconvolutionBlind Source SeparationMinimum Mutual Information MethodsWiener Systemshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1When dealing with nonlinear blind processing algorithms (deconvolution or post-nonlinear source separation) complex mathematical estimations must be done giving as a result very slow algorithms. This is the case, for example, in speech processing, spike signals deconvolution or microarray data analysis. In this paper, we propose a simple method to reduce computational time for the inversion of Wiener systems or the separation of post-nonlinear mixtures, by using a linear approximation in a minimum-mutual information algorithm. Simulation results demonstrate that linear spline interpolation is fast and accurate, obtaining very good results (similar to those obtained without approximation) while computational time is dramatically decreased. On the other hand, cubic spline interpolation also obtains similar good results, but due to its intrinsically complexity the global algorithm is much more slow and hence not useful for our purpose.Fil: Caiafa, Cesar Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - Conicet - la Plata. Instituto Argentino de Radioastronomia (i); ArgentinaFil: Sole-Casals, Jordi. Universidad de Vic; EspañaSpringer2013-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/4091Caiafa, Cesar Federico; Sole-Casals, Jordi ; A fast gradient approximation for nonlinear blind signal processing; Springer; Cognitive Computation; 5; 4; 12-2013; 483-4921866-9964enginfo:eu-repo/semantics/altIdentifier/doi/info:eu-repo/semantics/altIdentifier/doi/10.1007/s12559-012-9192-xinfo:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007/s12559-012-9192-xinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T14:41:21Zoai:ri.conicet.gov.ar:11336/4091instacron: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 14:41:22.205CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A fast gradient approximation for nonlinear blind signal processing
title A fast gradient approximation for nonlinear blind signal processing
spellingShingle A fast gradient approximation for nonlinear blind signal processing
Caiafa, Cesar Federico
Blind Deconvolution
Blind Source Separation
Minimum Mutual Information Methods
Wiener Systems
title_short A fast gradient approximation for nonlinear blind signal processing
title_full A fast gradient approximation for nonlinear blind signal processing
title_fullStr A fast gradient approximation for nonlinear blind signal processing
title_full_unstemmed A fast gradient approximation for nonlinear blind signal processing
title_sort A fast gradient approximation for nonlinear blind signal processing
dc.creator.none.fl_str_mv Caiafa, Cesar Federico
Sole-Casals, Jordi
author Caiafa, Cesar Federico
author_facet Caiafa, Cesar Federico
Sole-Casals, Jordi
author_role author
author2 Sole-Casals, Jordi
author2_role author
dc.subject.none.fl_str_mv Blind Deconvolution
Blind Source Separation
Minimum Mutual Information Methods
Wiener Systems
topic Blind Deconvolution
Blind Source Separation
Minimum Mutual Information Methods
Wiener Systems
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv When dealing with nonlinear blind processing algorithms (deconvolution or post-nonlinear source separation) complex mathematical estimations must be done giving as a result very slow algorithms. This is the case, for example, in speech processing, spike signals deconvolution or microarray data analysis. In this paper, we propose a simple method to reduce computational time for the inversion of Wiener systems or the separation of post-nonlinear mixtures, by using a linear approximation in a minimum-mutual information algorithm. Simulation results demonstrate that linear spline interpolation is fast and accurate, obtaining very good results (similar to those obtained without approximation) while computational time is dramatically decreased. On the other hand, cubic spline interpolation also obtains similar good results, but due to its intrinsically complexity the global algorithm is much more slow and hence not useful for our purpose.
Fil: Caiafa, Cesar Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico - Conicet - la Plata. Instituto Argentino de Radioastronomia (i); Argentina
Fil: Sole-Casals, Jordi. Universidad de Vic; España
description When dealing with nonlinear blind processing algorithms (deconvolution or post-nonlinear source separation) complex mathematical estimations must be done giving as a result very slow algorithms. This is the case, for example, in speech processing, spike signals deconvolution or microarray data analysis. In this paper, we propose a simple method to reduce computational time for the inversion of Wiener systems or the separation of post-nonlinear mixtures, by using a linear approximation in a minimum-mutual information algorithm. Simulation results demonstrate that linear spline interpolation is fast and accurate, obtaining very good results (similar to those obtained without approximation) while computational time is dramatically decreased. On the other hand, cubic spline interpolation also obtains similar good results, but due to its intrinsically complexity the global algorithm is much more slow and hence not useful for our purpose.
publishDate 2013
dc.date.none.fl_str_mv 2013-12
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/11336/4091
Caiafa, Cesar Federico; Sole-Casals, Jordi ; A fast gradient approximation for nonlinear blind signal processing; Springer; Cognitive Computation; 5; 4; 12-2013; 483-492
1866-9964
url http://hdl.handle.net/11336/4091
identifier_str_mv Caiafa, Cesar Federico; Sole-Casals, Jordi ; A fast gradient approximation for nonlinear blind signal processing; Springer; Cognitive Computation; 5; 4; 12-2013; 483-492
1866-9964
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/
info:eu-repo/semantics/altIdentifier/doi/10.1007/s12559-012-9192-x
info:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007/s12559-012-9192-x
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 Springer
publisher.none.fl_str_mv Springer
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 13.22299

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