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

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.