A fast nearest neighbor search algorithm based on vector quantization

Abstract : In this article, we propose a new fast nearest neighbor search algorithm, based on vector quantization. Like many other branch and bound search algorithms [1,10], a preprocessing recursively partitions the data set into disjointed subsets until the number of points in each part is small enough. In doing so, a search-tree data structure is built. This preliminary recursive data-set partition is based on the vector quantization of the empirical distribution of the initial data-set. Unlike previously cited methods, this kind of partitions does not a priori allow to eliminate several brother nodes in the search tree with a single test. To overcome this difficulty, we propose an algorithm to reduce the number of tested brother nodes to a minimal list that we call ''friend Voronoi cells''. The complete description of the method requires a deeper insight into the properties of Delaunay triangulations and Voronoi diagrams
Type de document :
Pré-publication, Document de travail
2011
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https://hal.archives-ouvertes.fr/hal-00595468
Contributeur : Sylvain Corlay <>
Soumis le : mardi 24 mai 2011 - 18:29:49
Dernière modification le : lundi 29 mai 2017 - 14:23:50
Document(s) archivé(s) le : jeudi 25 août 2011 - 02:27:47

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quantization_tree.pdf
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  • HAL Id : hal-00595468, version 1
  • ARXIV : 1105.4953

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UPMC | INSMI | PMA | USPC

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Sylvain Corlay. A fast nearest neighbor search algorithm based on vector quantization. 2011. <hal-00595468>

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