A method for continuous-range sequence analysis with Jensen-Shannon divergence

Autores
Ré, Miguel A.; Aguirre Varela, Guillermo G.
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión borrador
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: Ré, Miguel A.. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería. Córdoba; Argentina.
Fil: Aguirre Varela, Guillermo G.. Universidad Nacional de Córdoba. Facultad de Mátematicas, Astronomía y Computación. Córdoba; Argentina.
Peer Reviewed
Materia
mutual information
sequence segmentation
set comparison
Nivel de accesibilidad
acceso abierto
Condiciones de uso
2024-04-11T21:20:26Z
Repositorio
Repositorio Institucional Abierto (UTN)
Institución
Universidad Tecnológica Nacional
OAI Identificador
oai:ria.utn.edu.ar:20.500.12272/10469
Acceder
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network_name_str Repositorio Institucional Abierto (UTN)
spelling A method for continuous-range sequence analysis with Jensen-Shannon divergenceRé, Miguel A.Aguirre Varela, Guillermo G.mutual informationsequence segmentationset comparisonMutual 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: Ré, Miguel A.. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería. Córdoba; Argentina.Fil: Aguirre Varela, Guillermo G.. Universidad Nacional de Córdoba. Facultad de Mátematicas, Astronomía y Computación. Córdoba; Argentina.Peer Reviewed2024-04-11T21:20:26Z2024-04-11T21:20:26Z2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articulopdfapplication/pdfhttp://hdl.handle.net/20.500.12272/10469-enginfo:eu-repo/semantics/openAccess2024-04-11T21:20:26Zhttp://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 InternacionalRé, Miguel A._X_Atribución (Attribution): En cualquier explotación de la obra autorizada por la licencia será necesario reconocer la autoría (obligatoria en todos los casos). _X_No comercial (Non Commercial): La explotación de la obra queda limitada a usos no comerciales. _X_Sin obras derivadas (No Derivate Works): La autorización para explotar la obra no incluye la posibilidad de crear una obra derivada (traducciones, adaptaciones, etc.). _X_Compartir igual (Share Alike): La explotación autorizada incluye la creación de obras derivadas siempre que se mantenga la misma licencia al ser divulgadas.reponame:Repositorio Institucional Abierto (UTN)instname:Universidad Tecnológica Nacional2026-09-24T12:46:01Zoai:ria.utn.edu.ar:20.500.12272/10469instacron:UTNInstitucionalhttp://ria.utn.edu.ar/Universidad públicaNo correspondehttp://ria.utn.edu.ar/oaigestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:a2026-09-24 12:46:01.779Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacionalfalse
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
Ré, Miguel A.
mutual information
sequence segmentation
set comparison
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 Ré, Miguel A.
Aguirre Varela, Guillermo G.
author Ré, Miguel A.
author_facet Ré, Miguel A.
Aguirre Varela, Guillermo G.
author_role author
author2 Aguirre Varela, Guillermo G.
author2_role author
dc.subject.none.fl_str_mv mutual information
sequence segmentation
set comparison
topic mutual information
sequence segmentation
set comparison
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: Ré, Miguel A.. Universidad Tecnológica Nacional. Facultad Regional Córdoba. Centro de Investigación en Informática para la Ingeniería. Córdoba; Argentina.
Fil: Aguirre Varela, Guillermo G.. Universidad Nacional de Córdoba. Facultad de Mátematicas, Astronomía y Computación. Córdoba; Argentina.
Peer Reviewed
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
2024-04-11T21:20:26Z
2024-04-11T21:20:26Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/draft
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str draft
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12272/10469
-
url http://hdl.handle.net/20.500.12272/10469
identifier_str_mv -
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
2024-04-11T21:20:26Z
http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Ré, Miguel A.
_X_Atribución (Attribution): En cualquier explotación de la obra autorizada por la licencia será necesario reconocer la autoría (obligatoria en todos los casos). _X_No comercial (Non Commercial): La explotación de la obra queda limitada a usos no comerciales. _X_Sin obras derivadas (No Derivate Works): La autorización para explotar la obra no incluye la posibilidad de crear una obra derivada (traducciones, adaptaciones, etc.). _X_Compartir igual (Share Alike): La explotación autorizada incluye la creación de obras derivadas siempre que se mantenga la misma licencia al ser divulgadas.
eu_rights_str_mv openAccess
rights_invalid_str_mv 2024-04-11T21:20:26Z
http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Ré, Miguel A.
_X_Atribución (Attribution): En cualquier explotación de la obra autorizada por la licencia será necesario reconocer la autoría (obligatoria en todos los casos). _X_No comercial (Non Commercial): La explotación de la obra queda limitada a usos no comerciales. _X_Sin obras derivadas (No Derivate Works): La autorización para explotar la obra no incluye la posibilidad de crear una obra derivada (traducciones, adaptaciones, etc.). _X_Compartir igual (Share Alike): La explotación autorizada incluye la creación de obras derivadas siempre que se mantenga la misma licencia al ser divulgadas.
dc.format.none.fl_str_mv pdf
application/pdf
dc.source.none.fl_str_mv reponame:Repositorio Institucional Abierto (UTN)
instname:Universidad Tecnológica Nacional
reponame_str Repositorio Institucional Abierto (UTN)
collection Repositorio Institucional Abierto (UTN)
instname_str Universidad Tecnológica Nacional
repository.name.fl_str_mv Repositorio Institucional Abierto (UTN) - Universidad Tecnológica Nacional
repository.mail.fl_str_mv gestionria@rec.utn.edu.ar; fsuarez@rec.utn.edu.ar
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score 13.265058

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