A fast nearest neighbor search algorithm based on vector quantization

Sylvain Corlay (1)

1 LPMA - Laboratoire de Probabilités et Modèles Aléatoires

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

Mots clés: vector quantization; fast nearest neighbor search; Voronoi diagram; Delaunay triangulation; principal component analysis

Domaines: Probabilités [math.PR]

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