A method for continuous-range sequence analysis with jensen-shannon divergence

Autores
Re, Miguel Angel; Aguirre Varela, Guillermo Gabriel
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Mutual Information (MI) is a useful Information Theory tool for the recognition of mutual dependence between data sets. Several methods have been developed fore estimation of MI when both data sets are of the discrete type or when both are of the continuous type. However, MI estimation between a discrete range data set and a continuous range data set has not received so much attention. We therefore present here a method for the estimation of MI for this case, based on the kernel density approximation. This calculation may be of interest in diverse contexts. Since MI is closely related to the Jensen Shannon divergence, the method developed here is of particular interest in the problems of sequence segmentation and set comparisons.
Fil: Re, Miguel Angel. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Física. Grupo de Física de la Atmosfera; Argentina
Fil: Aguirre Varela, Guillermo Gabriel. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Física. Grupo de Física de la Atmosfera; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina
Materia
ENTROPIC DISTANCE
SEQUENCE SEGMENTATION
JENSEN-SHANNON DIVERGENCE
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:11336/172534
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A method for continuous-range sequence analysis with jensen-shannon divergenceRe, Miguel AngelAguirre Varela, Guillermo GabrielENTROPIC DISTANCESEQUENCE SEGMENTATIONJENSEN-SHANNON DIVERGENCEhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Mutual Information (MI) is a useful Information Theory tool for the recognition of mutual dependence between data sets. Several methods have been developed fore estimation of MI when both data sets are of the discrete type or when both are of the continuous type. However, MI estimation between a discrete range data set and a continuous range data set has not received so much attention. We therefore present here a method for the estimation of MI for this case, based on the kernel density approximation. This calculation may be of interest in diverse contexts. Since MI is closely related to the Jensen Shannon divergence, the method developed here is of particular interest in the problems of sequence segmentation and set comparisons.Fil: Re, Miguel Angel. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Física. Grupo de Física de la Atmosfera; ArgentinaFil: Aguirre Varela, Guillermo Gabriel. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Física. Grupo de Física de la Atmosfera; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; ArgentinaInstituto de Física de Líquidos y Sistemas Biológicos2021-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/172534Re, Miguel Angel; Aguirre Varela, Guillermo Gabriel; A method for continuous-range sequence analysis with jensen-shannon divergence; Instituto de Física de Líquidos y Sistemas Biológicos; Papers In Physics; 13; 130001; 2-2021; 1-101852-4249CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.papersinphysics.org/papersinphysics/article/view/638info:eu-repo/semantics/altIdentifier/doi/10.4279/pip.130001info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:35:47Zoai:ri.conicet.gov.ar:11336/172534instacron: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 10:35:48.242CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A method for continuous-range sequence analysis with jensen-shannon divergence
title A method for continuous-range sequence analysis with jensen-shannon divergence
spellingShingle A method for continuous-range sequence analysis with jensen-shannon divergence
Re, Miguel Angel
ENTROPIC DISTANCE
SEQUENCE SEGMENTATION
JENSEN-SHANNON DIVERGENCE
title_short A method for continuous-range sequence analysis with jensen-shannon divergence
title_full A method for continuous-range sequence analysis with jensen-shannon divergence
title_fullStr A method for continuous-range sequence analysis with jensen-shannon divergence
title_full_unstemmed A method for continuous-range sequence analysis with jensen-shannon divergence
title_sort A method for continuous-range sequence analysis with jensen-shannon divergence
dc.creator.none.fl_str_mv Re, Miguel Angel
Aguirre Varela, Guillermo Gabriel
author Re, Miguel Angel
author_facet Re, Miguel Angel
Aguirre Varela, Guillermo Gabriel
author_role author
author2 Aguirre Varela, Guillermo Gabriel
author2_role author
dc.subject.none.fl_str_mv ENTROPIC DISTANCE
SEQUENCE SEGMENTATION
JENSEN-SHANNON DIVERGENCE
topic ENTROPIC DISTANCE
SEQUENCE SEGMENTATION
JENSEN-SHANNON DIVERGENCE
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Mutual Information (MI) is a useful Information Theory tool for the recognition of mutual dependence between data sets. Several methods have been developed fore estimation of MI when both data sets are of the discrete type or when both are of the continuous type. However, MI estimation between a discrete range data set and a continuous range data set has not received so much attention. We therefore present here a method for the estimation of MI for this case, based on the kernel density approximation. This calculation may be of interest in diverse contexts. Since MI is closely related to the Jensen Shannon divergence, the method developed here is of particular interest in the problems of sequence segmentation and set comparisons.
Fil: Re, Miguel Angel. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Física. Grupo de Física de la Atmosfera; Argentina
Fil: Aguirre Varela, Guillermo Gabriel. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Física. Grupo de Física de la Atmosfera; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina
description Mutual Information (MI) is a useful Information Theory tool for the recognition of mutual dependence between data sets. Several methods have been developed fore estimation of MI when both data sets are of the discrete type or when both are of the continuous type. However, MI estimation between a discrete range data set and a continuous range data set has not received so much attention. We therefore present here a method for the estimation of MI for this case, based on the kernel density approximation. This calculation may be of interest in diverse contexts. Since MI is closely related to the Jensen Shannon divergence, the method developed here is of particular interest in the problems of sequence segmentation and set comparisons.
publishDate 2021
dc.date.none.fl_str_mv 2021-02
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/11336/172534
Re, Miguel Angel; Aguirre Varela, Guillermo Gabriel; A method for continuous-range sequence analysis with jensen-shannon divergence; Instituto de Física de Líquidos y Sistemas Biológicos; Papers In Physics; 13; 130001; 2-2021; 1-10
1852-4249
CONICET Digital
CONICET
url http://hdl.handle.net/11336/172534
identifier_str_mv Re, Miguel Angel; Aguirre Varela, Guillermo Gabriel; A method for continuous-range sequence analysis with jensen-shannon divergence; Instituto de Física de Líquidos y Sistemas Biológicos; Papers In Physics; 13; 130001; 2-2021; 1-10
1852-4249
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.papersinphysics.org/papersinphysics/article/view/638
info:eu-repo/semantics/altIdentifier/doi/10.4279/pip.130001
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
dc.publisher.none.fl_str_mv Instituto de Física de Líquidos y Sistemas Biológicos
publisher.none.fl_str_mv Instituto de Física de Líquidos y Sistemas Biológicos
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 13.070432

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