dc.creatorB. Deignan,Paul
dc.date2011-12-01
dc.date.accessioned2023-09-25T14:09:47Z
dc.date.available2023-09-25T14:09:47Z
dc.identifierhttp://www.scielo.sa.cr/scielo.php?script=sci_arttext&pid=S1409-24332011000200006
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8814049
dc.descriptionAs 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 continuous 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.
dc.formattext/html
dc.languageen
dc.publisherCentro de Investigaciones en Matemática Pura y Aplicada (CIMPA) y Escuela de Matemática, San José, Costa Rica.
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourceRevista de Matemática Teoría y Aplicaciones v.18 n.2 2011
dc.subjectInformation theory
dc.subjectdata association
dc.subjectfusion
dc.subjectestimation
dc.subjectentropy
dc.titleSensor fusion using entropic measures of dependence
dc.typeinfo:eu-repo/semantics/article


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