A modification of the LAESA algorithm for approximated k-NN classification

Authors:
Francisco Moreno-Seco;Luisa Milcó;Jose Oncina
Affiliations:
Departamento de Lenguajes y Sistemas Informáticos, Universidad de Alicante, E-03071 Alicante, Spain;Departamento de Lenguajes y Sistemas Informáticos, Universidad de Alicante, E-03071 Alicante, Spain;Departamento de Lenguajes y Sistemas Informáticos, Universidad de Alicante, E-03071 Alicante, Spain
Venue:
Pattern Recognition Letters
Year:
2003

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Abstract

Nearest-neighbour (NN) and k-nearest-neighbours (k-NN) techniques are widely used in many pattern recognition classification tasks. The linear approximating and eliminating search algorithm (LAESA) is a fast NN algorithm which does not assume that the prototypes are defined in a vector space; it only makes use of some of the distance properties (mainly the triangle inequality) in order to avoid distance computations.In this work we propose an improvement of LAESA that uses k neighbours in order to approach to the accuracy of a k-NN classifier, and computes the same number of distances than the LAESA preserving the time and space complexity independent from k.