Locality-sensitive hashing scheme based on p-stable distributions

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Locality-sensitive hashing scheme based on p-stable distributions (2004)

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by Mayur Datar , Piotr Indyk

Venue:	In SCG ’04: Proceedings of the twentieth annual symposium on Computational geometry
Citations:	519 - 8 self

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@INPROCEEDINGS{Datar04locality-sensitivehashing,
    author = {Mayur Datar and Piotr Indyk},
    title = {Locality-sensitive hashing scheme based on p-stable distributions},
    booktitle = {In SCG ’04: Proceedings of the twentieth annual symposium on Computational geometry},
    year = {2004},
    pages = {253--262},
    publisher = {ACM Press}
}

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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.

Keyphrases

p-stable distribution data structure approximate nearest neighbor problem stable distribution large factor running time certain bounded growth condition novel locality-sensitive hashing scheme efficient approximate nn algorithm euclidean space query time bound neigbhor time synthetic data set lsh scheme

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