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.
KeywordsDistance Computation Neighbour Algorithm Database Size Pattern Recognition Letter Dissimilarity Function
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