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

Authors:
Francisco Moreno-Seco;Luisa Micó;José Oncina
Affiliations:
-;-;-
Venue:
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2002

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Abstract

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.