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Efficient Search for Approximate Nearest Neighbor in High Dimensional Spaces (1998)
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BibTeX
@MISC{Kushilevitz98efficientsearch,
author = {Eyal Kushilevitz and Rafail Ostrovsky and Yuval Rabani},
title = {Efficient Search for Approximate Nearest Neighbor in High Dimensional Spaces},
year = {1998}
}
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Abstract
We address the problem of designing data structures that allow efficient search for approximate nearest neighbors. More specifically, given a database consisting of a set of vectors in some high dimensional Euclidean space, we want to construct a space-efficient data structure that would allow us to search, given a query vector, for the closest or nearly closest vector in the database. We also address this problem when distances are measured by the L 1 norm, and in the Hamming cube. Significantly improving and extending recent results of Kleinberg, we construct data structures whose size is polynomial in the size of the database, and search algorithms that run in time nearly linear or nearly quadratic in the dimension (depending on the case; the extra factors are polylogarithmic in the size of the database). Computer Science Department, Technion --- IIT, Haifa 32000, Israel. Email: eyalk@cs.technion.ac.il y Bell Communications Research, MCC-1C365B, 445 South Street, Morristown, NJ ...
Keyphrases
efficient search approximate nearest neighbor high dimensional space data structure south street extra factor query vector high dimensional euclidean space technion iit il bell communication research database consisting computer science department hamming cube space-efficient data structure search algorithm recent result
