PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Combining Elimination Rules in Tree-Based Nearest Neighbor Search Algorithms
Eva Gómez-Ballester, Luisa Mico, Franck Thollard, Jose Oncina and Francisco Moreno-Seco
In: Structural, Syntactic, and Statistical Pattern Recognition Workshop, 18-20 Aug 2010, Cesme, Turkey.


A common activity in many pattern recognition tasks, image processing or clustering techniques involves searching a labeled data set looking for the nearest point to a given unlabeled sample. To reduce the computational overhead when the naive exhaustive search is applied, some fast nearest neighbor search (NNS) algorithms have appeared in the last years. Depending on the structure used to store the training set (usually a tree), different strategies to speed up the search have been defined. In this paper, a new algorithm based on the combination of different pruning rules is proposed. An experimental evaluation and comparison of its behavior with respect to other techniques has been performed, using both real and artificial data.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:7415
Deposited By:Luisa Mico
Deposited On:17 March 2011