Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality

Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality (1998)

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by Piotr Indyk , Rajeev Motwani

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Datum	Value	Source
TITLE	Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality	user correction - Legacy Corrections
AUTHOR NAME	Piotr Indyk	user correction
AUTHOR AFFIL	Department of Computer Science; Stanford University	user correction
AUTHOR ADDR	Stanford, CA 94305	user correction
AUTHOR NAME	Rajeev Motwani	user correction
AUTHOR AFFIL	Department of Computer Science; Stanford University	user correction
AUTHOR ADDR	Stanford, CA 94305	user correction
ABSTRACT	The nearest neighbor problem is the following: Given a set of n points P = fp 1 ; : : : ; png in some metric space X, preprocess P so as to efficiently answer queries which require finding the point in P closest to a query point q 2 X. We focus on the particularly interesting case of the d-dimensional Euclidean space where X = ! d under some l p norm. Despite decades of effort, the current solutions are far from satisfactory; in fact, for large d, in theory or in practice, they provide little improvement over the brute-force algorithm which compares the query point to each data point. Of late, there has been some interest in the approximate nearest neighbors problem, which is: Find a point p 2 P that is an ffl-approximate nearest neighbor of the query q in that for all p 0 2 P , d(p; q) (1 + ffl)d(p 0 ; q). We present two algorithmic results for the approximate version that significantly improve the known bounds: (a) preprocessing cost polynomial in n and d, and a trul...	user correction - Legacy Corrections
YEAR	1998	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	604--613	INFERENCE
CITATIONS	57 found	ParsCit 1.0