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Prototype modelling of body surface maps using tangent distance

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Show simple item record Patterson, Richard MacKenzie, Gilbert 2013-01-17T16:48:45Z 2013-01-17T16:48:45Z 1999
dc.description peer-reviewed en_US
dc.description.abstract In recent years the tangent distance approach of Simard et al (1993) to pattern recognition tasks such as handwritten character recognition has proven successful. Simard's distance measure could be made locally invariant to any set of transformations of the input and when implemented in one-nearest-neighbour classification of handwritten digits, outperformed all other classification schemes. Hastie et al (1996) propose prototype models which generalise the concept of a centroid of a set of images in the Euclidian metric to a low-dimensional hyperplane in the tangent metric, and these prototypes can be used to reduce lookup time in classification. We propose to apply and extend the tangent distance approach to classify a set of body surface maps, which are recordings of the electrical activity of the heart, of a large number of patients with various cardiac conditions. Using a grid of p electrodes attached to the anterior chest, we calculate a number of p-dimensional observation vectors for each patient and classify input maps on the basis of overall distance of map to prototypes derived from training set maps over all included observation vectors. en_US
dc.language.iso eng en_US
dc.publisher IWSM en_US
dc.relation.ispartofseries Proceedings of the 14th International Workshop on Statistical Modelling;
dc.subject classification en_US
dc.subject invariance dimension en_US
dc.subject prototype models en_US
dc.title Prototype modelling of body surface maps using tangent distance en_US
dc.type info:eu-repo/semantics/conferenceObject en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US

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