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Similarity search in high dimensions via hashing (1999)
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| Citations: | 641 - 10 self |
BibTeX
@INPROCEEDINGS{Gionis99similaritysearch,
author = {Aristides Gionis and Piotr Indyk and Rajeev Motwani},
title = {Similarity search in high dimensions via hashing},
booktitle = {},
year = {1999},
pages = {518--529}
}
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Abstract
The nearest- or near-neighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing interest in building search/index structures for performing similarity search over high-dimensional data, e.g., image databases, document collections, time-series databases, and genome databases. Unfortunately, all known techniques for solving this problem fall prey to the \curse of dimensionality. " That is, the data structures scale poorly with data dimensionality; in fact, if the number of dimensions exceeds 10 to 20, searching in k-d trees and related structures involves the inspection of a large fraction of the database, thereby doing no better than brute-force linear search. It has been suggested that since the selection of features and the choice of a distance metric in typical applications is rather heuristic, determining an approximate nearest neighbor should su ce for most practical purposes. In this paper, we examine a novel scheme for approximate similarity search based on hashing. The basic idea is to hash the points
Keyphrases
similarity search high dimension document collection approximate similarity search problem fall prey data dimensionality high-dimensional data dimension exceeds genome database image database typical application large fraction brute-force linear search practical purpose large variety data structure novel scheme near-neighbor query problem search index structure basic idea k-d tree database application related structure time-series database
