Pattern recognition via projection – based k – NN rules

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dc.creator.AutorFraiman, Ricardo
dc.creator.AutorJustel, Ana
dc.creator.AutorSvarc, Marcela
dc.date.accessioned2011-09-19T13:48:44Z
dc.date.available2011-09-19T13:48:44Z
dc.date.issued2008-06
dc.descriptionFil: Fraiman, Ricardo. Universidad de San Andrés. Departamento de Matemática y Ciencias; Argentina.
dc.descriptionFil: Justel, Ana. Universidad de San Andrés. Departamento de Matemática y Ciencias; Argentina.
dc.descriptionFil: Svarc, Marcela. Universidad de San Andrés. Departamento de Matemática y Ciencias; Argentina.
dc.description.abstractWe introduce a new procedure for pattern recognition, based on the concepts of random projections and nearest neighbors. It can be thought as an improvement of the classical nearest neighbors classification rules. Besides the concept of neighbors we introduce the notion of district, a larger set which will be projected. Then we apply one dimensional k-NN methods to the projected data on randomly selected directions. In this way we are able to provide a method with some robustness properties and more accurate to handle high dimensional data. The procedure is also universally consistent. We challenge the method with the Isolet data where we obtain a very high classification score.
dc.formatapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10908/553
dc.languageeng
dc.publisherUniversidad de San Andrés. Departamento de Matemáticas y Ciencias
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMultivariate analysis
dc.subjectRobust statistics
dc.subjectPattern perception
dc.titlePattern recognition via projection – based k – NN rules
dc.typeDocumento de Trabajo
dc.typeinfo:eu-repo/semantics/workingPaper
dc.typeinfo:ar-repo/semantics/documento de trabajo
dc.typeinfo:eu-repo/semantics/draft
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