A Fast Approximately k—Nearest—Neighbour Search Algorithm For Clasification Tasks
The k-nearest-neighbour (k-NN) search algorithm is widely used in pattern classification tasks. A large set of fast k-NN search algorithms have been developed in order to obtain lower error rates. Most of them are extensions of fast NN search algorithms where the condition of finding exactly the k nearest neighbours is imposed. All these algorithms calculate a number of distances that increases with k. Also, a vector-space representation is usually needed in these algorithms. If the condition of finding exactly the k nearest neighbours is relaxed, further reductions on the number of distance computations can be obtained. In this work we propose a modification of the LAESA (Linear Approximating and Eliminating Search Algorithm, a fast NN search algorithm for metric spaces) in order to use a certain neighbourhood for lowering error rates and reduce the number of distance computations at the same time.
KeywordsNearest Neighbour Metric Spaces Pattern Recognition
- 1.Aibar, P., Juan, A., Vidal, E.: Extensions to the approximating and eliminating search algorithm (AESA) for finding k-nearest-neighbours. New Advances and Trends in Speech Recognition and Coding (1993) 23–28Google Scholar
- 2.Duda, R., Hart, P.: Pattern Classification and Scene Analysis. Wiley (1973)Google Scholar
- 3.Jain, A.K., Dubes, R.C.: Algorithms for clustering data. Prentice-Hall (1988)Google Scholar
- 6.Moreno-Seco, F., Oncina, J., Micó, L.: Improving the LAESA algorithm error rates. In: Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Análisis de Imágenes, Bilbao (1999) 413–419Google Scholar