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A Fast Approximately k—Nearest—Neighbour Search Algorithm For Clasification Tasks

  • Francisco Moreno-Seco
  • Luisa Micó
  • José Oncina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)

Abstract

The k-nearest-neighbour (k-NN) search algorithm is widely used in pattern classification tasks. A large set of fast k-NN search algorithms have been developed in order to obtain lower error rates. Most of them are extensions of fast NN search algorithms where the condition of finding exactly the k nearest neighbours is imposed. All these algorithms calculate a number of distances that increases with k. Also, a vector-space representation is usually needed in these algorithms. If the condition of finding exactly the k nearest neighbours is relaxed, further reductions on the number of distance computations can be obtained. In this work we propose a modification of the LAESA (Linear Approximating and Eliminating Search Algorithm, a fast NN search algorithm for metric spaces) in order to use a certain neighbourhood for lowering error rates and reduce the number of distance computations at the same time.

Keywords

Nearest Neighbour Metric Spaces Pattern Recognition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Francisco Moreno-Seco
    • 1
  • Luisa Micó
    • 1
  • José Oncina
    • 1
  1. 1.Dept. Lenguajes y Sistemas InformáticosUniversidad de AlicanteAlicanteSpain

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