Diffuse outlier time series detection technique for functional magnetic resonance imaging

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 acc...

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Detalles Bibliográficos
Autores principales:	Giacomantone, Javier, Tarutina, Tatiana, De Giusti, Armando Eduardo
Formato:	Objeto de conferencia
Lenguaje:	Español
Publicado:	2010
Materias:	Ciencias Informáticas Time Series Functional Magnetic Resonance Imaging classification Support Vector Machines Robust Anisotropic Diffusión Time series analysis
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/19379
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-19379
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Español
topic	Ciencias Informáticas Time Series Functional Magnetic Resonance Imaging classification Support Vector Machines Robust Anisotropic Diffusión Time series analysis
spellingShingle	Ciencias Informáticas Time Series Functional Magnetic Resonance Imaging classification Support Vector Machines Robust Anisotropic Diffusión Time series analysis Giacomantone, Javier Tarutina, Tatiana De Giusti, Armando Eduardo Diffuse outlier time series detection technique for functional magnetic resonance imaging
topic_facet	Ciencias Informáticas Time Series Functional Magnetic Resonance Imaging classification Support Vector Machines Robust Anisotropic Diffusión Time series analysis
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.
format	Objeto de conferencia Objeto de conferencia
author	Giacomantone, Javier Tarutina, Tatiana De Giusti, Armando Eduardo
author_facet	Giacomantone, Javier Tarutina, Tatiana De Giusti, Armando Eduardo
author_sort	Giacomantone, Javier
title	Diffuse outlier time series detection technique for functional magnetic resonance imaging
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
publishDate	2010
url	http://sedici.unlp.edu.ar/handle/10915/19379
work_keys_str_mv	AT giacomantonejavier diffuseoutliertimeseriesdetectiontechniqueforfunctionalmagneticresonanceimaging AT tarutinatatiana diffuseoutliertimeseriesdetectiontechniqueforfunctionalmagneticresonanceimaging AT degiustiarmandoeduardo diffuseoutliertimeseriesdetectiontechniqueforfunctionalmagneticresonanceimaging
bdutipo_str	Repositorios
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Diffuse outlier time series detection technique for functional magnetic resonance imaging

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