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Locality-sensitive hashing scheme based on p-stable distributions

By Mayur Datar and Piotr Indyk

Abstract

inÇÐÓ�Ò We present a novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem underÐÔnorm, based onÔstable distributions. Our scheme improves the running time of the earlier algorithm for the case of theÐnorm. It also yields the first known provably efficient approximate NN algorithm for the caseÔ�. We also show that the algorithm finds the exact near neigbhor time for data satisfying certain “bounded growth ” condition. Unlike earlier schemes, our LSH scheme works directly on points in the Euclidean space without embeddings. Consequently, the resulting query time bound is free of large factors and is simple and easy to implement. Our experiments (on synthetic data sets) show that the our data structure is up to 40 times faster than��-tree

Topics: Categories and Subject Descriptors E.1 [Data, Data Structures, F.0 [Theory of Computation, General General Terms Algorithms, Experimentation, Design, Performance, Theory Keywords Sublinear Algorithm, Approximate Nearest Neighbor, Locally Sensitive Hashing, Ô-Stable Distributions
Publisher: ACM Press
Year: 2004
OAI identifier: oai:CiteSeerX.psu:10.1.1.134.8167
Provided by: CiteSeerX
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