Sensor Fusion Using Entropic measures of Dependence

As opposed to standard methods of association which rely on measures of central dispersion, entropic measures quantify multivalued relations. This distinction is especially important when high fidelity models of the sensed phenomena do not exist. The properties of entropic measures are shown to fit...

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Autor Principal: Deignan, Paul B.
Formato: Artículo
Idioma: Español
Publicado: 2015
Acceso en línea: http://revistas.ucr.ac.cr/index.php/matematica/article/view/2099
http://hdl.handle.net/10669/13000
id RepoKERWA13000
recordtype dspace
spelling RepoKERWA130002017-08-08T18:50:25Z Sensor Fusion Using Entropic measures of Dependence Fusión Sensorial usando medidas Entrópicas de Dependencia Deignan, Paul B. As opposed to standard methods of association which rely on measures of central dispersion, entropic measures quantify multivalued relations. This distinction is especially important when high fidelity models of the sensed phenomena do not exist. The properties of entropic measures are shown to fit within the Bayesian framework of hierarchical sensor fusion. A method of estimating probabilistic structure for categorical and continuous valued measurements that is unbiased for finite data collections is presented. Additionally, a branch and bound method for optimal sensor suite selection suitable for either target refinement or anomaly detection is described. Finally, the methodology is applied against a known data set used in a standard data mining competition that features both sparse categorical and  ontinuous valued descriptors of a target. Excellent quantitative and computational results against this data set support the conclusion that the proposed methodology is promising for general purpose low level data fusion. Contrario a los métodos estándar de asociación que ligan medidas de dispersión central, las medidas de entropía cuantifican relaciones multivaluadas. Esta distinción es especialmente importante cuando no existen modelos de alta fidelidad de los fenómenos detectados. Se muestra que las propiedades de las medidas de entropía calzan en la marco Bayesiano de sensores jerárquicos de fusión. Se presenta un método de estimación de la estructura probabilística para medidas categóricas y continuas, el cual es insesgado para colecciones finitas de datos. Adicionalmente, se describe un método de ramificación y acotamiento de selección óptima del sensor apropiado tanto para refinamiento del objetivo como para detección de anomalías. Finalmente, la metodología es aplicada sobre un conjunto conocido de datos usados en una competencia estándar de minería de datos, que caracteriza tanto descriptores ralos categóricos como continuos de un objetivo. Excelentes resultados cuantitativos y computacionales con estos datos apoyan la conclusión de que la metodología propuesta es promisoria para propósitos generales con datos bajos niveles de fusión. 2015-05-19T18:59:46Z 2015-05-19T18:59:46Z 2011-07-26 00:00:00 2015-05-19T18:59:46Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://revistas.ucr.ac.cr/index.php/matematica/article/view/2099 http://hdl.handle.net/10669/13000 10.15517/rmta.v18i2.2099 es Revista de Matemática: Teoría y Aplicaciones Vol. 18 Núm. 2 2011 299-324 application/pdf
institution Universidad de Costa Rica
collection Repositorio KERWA
language Español
description As opposed to standard methods of association which rely on measures of central dispersion, entropic measures quantify multivalued relations. This distinction is especially important when high fidelity models of the sensed phenomena do not exist. The properties of entropic measures are shown to fit within the Bayesian framework of hierarchical sensor fusion. A method of estimating probabilistic structure for categorical and continuous valued measurements that is unbiased for finite data collections is presented. Additionally, a branch and bound method for optimal sensor suite selection suitable for either target refinement or anomaly detection is described. Finally, the methodology is applied against a known data set used in a standard data mining competition that features both sparse categorical and  ontinuous valued descriptors of a target. Excellent quantitative and computational results against this data set support the conclusion that the proposed methodology is promising for general purpose low level data fusion.
format Artículo
author Deignan, Paul B.
spellingShingle Deignan, Paul B.
Sensor Fusion Using Entropic measures of Dependence
author_sort Deignan, Paul B.
title Sensor Fusion Using Entropic measures of Dependence
title_short Sensor Fusion Using Entropic measures of Dependence
title_full Sensor Fusion Using Entropic measures of Dependence
title_fullStr Sensor Fusion Using Entropic measures of Dependence
title_full_unstemmed Sensor Fusion Using Entropic measures of Dependence
title_sort sensor fusion using entropic measures of dependence
publishDate 2015
url http://revistas.ucr.ac.cr/index.php/matematica/article/view/2099
http://hdl.handle.net/10669/13000
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