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
Document type :
Preprints, Working Papers, ...
2011
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-00595468
Contributor : Sylvain Corlay <>
Submitted on : Tuesday, May 24, 2011 - 6:29:49 PM
Last modification on : Wednesday, October 12, 2016 - 1:04:53 AM
Document(s) archivé(s) le : Thursday, August 25, 2011 - 2:27:47 AM

Files

quantization_tree.pdf
Files produced by the author(s)

Identifiers

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

Collections

UPMC | INSMI | PMA | USPC

Citation

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

Share

Metrics

Record views

309

Document downloads

337