Extending LAESA Fast Nearest Neighbour Algorithm to Find the k Nearest Neighbours

  • Francisco Moreno-Seco
  • Luisa Micó
  • Jose Oncina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)


Many pattern recognition tasks make use of the k nearest neighbour (k-NN) technique. In this paper we are interested on fast k-NN search algorithms that can work in any metric space i.e. they are not restricted to Euclidean-like distance functions. Only symmetric and triangle inequality properties are required for the distance.

A large set of such fast k-NN search algorithms have been developed during last years for the special case where k = 1. Some of them have been extended for the general case. This paper proposes an extension of LAESA (Linear Approximation Elimination Search Algorithm) to find the k-NN.


Distance Computation Neighbour Algorithm Database Size Pattern Recognition Letter Dissimilarity Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

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

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