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Utilization of singularity exponent in nearest neighbor based classifier

Author

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  • Marcel Jirina
  • Marcel Jirina

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

Suggested Citation

  • Marcel Jirina & Marcel Jirina, 2013. "Utilization of singularity exponent in nearest neighbor based classifier," Journal of Classification, Springer;The Classification Society, vol. 30(1), pages 3-29, April.
    • Handle: RePEc:spr:jclass:v:30:y:2013:i:1:p:3-29
    DOI: 10.1007/s00357-013-9121-z
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    Cited by:

    1. Hugo Proença & João C. Neves, 2017. "Fusing Vantage Point Trees and Linear Discriminants for Fast Feature Classification," Journal of Classification, Springer;The Classification Society, vol. 34(1), pages 85-107, April.
    2. Hossein Baloochian & Hamid Reza Ghaffary, 2019. "Multiclass Classification Based on Multi-criteria Decision-making," Journal of Classification, Springer;The Classification Society, vol. 36(1), pages 140-151, April.

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