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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/4091
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T09:47:31Zoai: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:34982025-09-29 09:47:32.066CONICET 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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1844613480881061888 |
score |
13.070432 |