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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
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Contributor : Sylvain Corlay <>
Submitted on : Tuesday, May 24, 2011 - 6:29:49 PM
Last modification on : Wednesday, December 9, 2020 - 3:07:46 PM
Long-term archiving on: : Thursday, August 25, 2011 - 2:27:47 AM


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


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



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