Diffuse outlier time series detection technique for functional magnetic resonance imaging
- Autores
- Giacomantone, Javier; Tarutina, Tatiana; De Giusti, Armando Eduardo
- Año de publicación
- 2010
- Idioma
- español castellano
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- We propose a new support vector machine (SVM) based method that improves the time series classi cation in magnetic resonance imaging (fMRI). We exploit the robust anisotropic di usion (RAD) technique to increase the classi cation performance of the one class support vector machine by taking into account the hypothesis of spatial relationship between active voxels. The proposed method was called Di use One Class Support Vector Machine (DOCSVM). DOCSVM method treats activated voxels as outliers and applies one class support vector machine to generate an activation map and RAD to include the neighborhood hypothesis, improving the classi cation and reducing the iteration steps with respect to RADSPM. We give a brief review of the main methods, present receiver operating characteristic (ROC) results and conclude suggesting further research alternatives.
Presentado en el I Workshop Procesamiento de señales y Sistemas de Tiempo Real (WPSTR)
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
Time Series
Functional Magnetic Resonance Imaging
classification
Support Vector Machines
Robust Anisotropic Diffusión
Time series analysis - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/19379
Ver los metadatos del registro completo
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Diffuse outlier time series detection technique for functional magnetic resonance imagingGiacomantone, JavierTarutina, TatianaDe Giusti, Armando EduardoCiencias InformáticasTime SeriesFunctional Magnetic Resonance ImagingclassificationSupport Vector MachinesRobust Anisotropic DiffusiónTime series analysisWe propose a new support vector machine (SVM) based method that improves the time series classi cation in magnetic resonance imaging (fMRI). We exploit the robust anisotropic di usion (RAD) technique to increase the classi cation performance of the one class support vector machine by taking into account the hypothesis of spatial relationship between active voxels. The proposed method was called Di use One Class Support Vector Machine (DOCSVM). DOCSVM method treats activated voxels as outliers and applies one class support vector machine to generate an activation map and RAD to include the neighborhood hypothesis, improving the classi cation and reducing the iteration steps with respect to RADSPM. We give a brief review of the main methods, present receiver operating characteristic (ROC) results and conclude suggesting further research alternatives.Presentado en el I Workshop Procesamiento de señales y Sistemas de Tiempo Real (WPSTR)Red de Universidades con Carreras en Informática (RedUNCI)2010-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf990-998http://sedici.unlp.edu.ar/handle/10915/19379spainfo:eu-repo/semantics/altIdentifier/isbn/978-950-9474-49-9info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:53:51Zoai:sedici.unlp.edu.ar:10915/19379Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:53:51.784SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Diffuse outlier time series detection technique for functional magnetic resonance imaging |
title |
Diffuse outlier time series detection technique for functional magnetic resonance imaging |
spellingShingle |
Diffuse outlier time series detection technique for functional magnetic resonance imaging Giacomantone, Javier Ciencias Informáticas Time Series Functional Magnetic Resonance Imaging classification Support Vector Machines Robust Anisotropic Diffusión Time series analysis |
title_short |
Diffuse outlier time series detection technique for functional magnetic resonance imaging |
title_full |
Diffuse outlier time series detection technique for functional magnetic resonance imaging |
title_fullStr |
Diffuse outlier time series detection technique for functional magnetic resonance imaging |
title_full_unstemmed |
Diffuse outlier time series detection technique for functional magnetic resonance imaging |
title_sort |
Diffuse outlier time series detection technique for functional magnetic resonance imaging |
dc.creator.none.fl_str_mv |
Giacomantone, Javier Tarutina, Tatiana De Giusti, Armando Eduardo |
author |
Giacomantone, Javier |
author_facet |
Giacomantone, Javier Tarutina, Tatiana De Giusti, Armando Eduardo |
author_role |
author |
author2 |
Tarutina, Tatiana De Giusti, Armando Eduardo |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas Time Series Functional Magnetic Resonance Imaging classification Support Vector Machines Robust Anisotropic Diffusión Time series analysis |
topic |
Ciencias Informáticas Time Series Functional Magnetic Resonance Imaging classification Support Vector Machines Robust Anisotropic Diffusión Time series analysis |
dc.description.none.fl_txt_mv |
We propose a new support vector machine (SVM) based method that improves the time series classi cation in magnetic resonance imaging (fMRI). We exploit the robust anisotropic di usion (RAD) technique to increase the classi cation performance of the one class support vector machine by taking into account the hypothesis of spatial relationship between active voxels. The proposed method was called Di use One Class Support Vector Machine (DOCSVM). DOCSVM method treats activated voxels as outliers and applies one class support vector machine to generate an activation map and RAD to include the neighborhood hypothesis, improving the classi cation and reducing the iteration steps with respect to RADSPM. We give a brief review of the main methods, present receiver operating characteristic (ROC) results and conclude suggesting further research alternatives. Presentado en el I Workshop Procesamiento de señales y Sistemas de Tiempo Real (WPSTR) Red de Universidades con Carreras en Informática (RedUNCI) |
description |
We propose a new support vector machine (SVM) based method that improves the time series classi cation in magnetic resonance imaging (fMRI). We exploit the robust anisotropic di usion (RAD) technique to increase the classi cation performance of the one class support vector machine by taking into account the hypothesis of spatial relationship between active voxels. The proposed method was called Di use One Class Support Vector Machine (DOCSVM). DOCSVM method treats activated voxels as outliers and applies one class support vector machine to generate an activation map and RAD to include the neighborhood hypothesis, improving the classi cation and reducing the iteration steps with respect to RADSPM. We give a brief review of the main methods, present receiver operating characteristic (ROC) results and conclude suggesting further research alternatives. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-10 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/19379 |
url |
http://sedici.unlp.edu.ar/handle/10915/19379 |
dc.language.none.fl_str_mv |
spa |
language |
spa |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/isbn/978-950-9474-49-9 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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application/pdf 990-998 |
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