Combining Elimination Rules in Tree-Based Nearest Neighbor Search Algorithms

Abstract : 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 unlabelled 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.
Type de document :
Communication dans un congrès
Hancock, Edwin and Wilson, Richard and Windeatt, Terry and Ulusoy, Ilkay and Escolano, Francisco. Structural, Syntactic, and Statistical Pattern Recognition, Aug 2010, Cesme, Izmir, Turkey. Springer Berlin / Heidelberg, 6218, pp.80-89, 2010, Lecture Notes in Computer Science
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https://hal-ujm.archives-ouvertes.fr/hal-00961322
Contributeur : Franck Thollard <>
Soumis le : vendredi 10 septembre 2010 - 15:47:50
Dernière modification le : mercredi 25 juillet 2018 - 14:05:30
Document(s) archivé(s) le : jeudi 30 juin 2011 - 13:26:28

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  • HAL Id : hal-00961322, version 2

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Eva Gómez-Ballester, Luisa Micó, Franck Thollard, Jose Oncina, Francisco Moreno-Seco. Combining Elimination Rules in Tree-Based Nearest Neighbor Search Algorithms. Hancock, Edwin and Wilson, Richard and Windeatt, Terry and Ulusoy, Ilkay and Escolano, Francisco. Structural, Syntactic, and Statistical Pattern Recognition, Aug 2010, Cesme, Izmir, Turkey. Springer Berlin / Heidelberg, 6218, pp.80-89, 2010, Lecture Notes in Computer Science. 〈hal-00961322v2〉

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