Utilization of singularity exponent in nearest neighbor based classifier

Marcel Jirina () and Marcel Jirina ()

Journal of Classification, 2013, vol. 30, issue 1, 3-29

Abstract: Classifiers serve as tools for classifying data into classes. They directly or indirectly take a distribution of data points around a given query point into account. To express the distribution of points from the viewpoint of distances from a given point, a probability distribution mapping function is introduced here. The approximation of this function in a form of a suitable power of the distance is presented. How to state this power—the distribution mapping exponent—is described. This exponent is used for probability density estimation in high-dimensional spaces and for classification. A close relation of the exponent to a singularity exponent is discussed. It is also shown that this classifier exhibits better behavior (classification accuracy) than other kinds of classifiers for some tasks. Copyright Springer Science+Business Media New York 2013

Keywords: Multivariate data; Probability density estimation; Classification; Probability distribution mapping function; Probability density mapping function; Power approximation (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00357-013-9121-z

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