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Title: Adaptive Estimation for Approximate k-Nearest-Neighbor Computations
Algorithms often carry out equally many computations for “easy” and “hard” problem instances. In particular, algorithms for finding nearest neighbors typically have the same running time regardless of the particular problem instance. In this paper, we consider the approximate k-nearest-neighbor problem, which is the problem of finding a subset of O(k) points in a given set of points that contains the set of k nearest neighbors of a given query point. We pro- pose an algorithm based on adaptively estimating the distances, and show that it is essentially optimal out of algorithms that are only allowed to adaptively estimate distances. We then demonstrate both theoretically and experimentally that the algorithm can achieve significant speedups relative to the naive method.
Authors:
; ;
Award ID(s):
1816986
Publication Date:
NSF-PAR ID:
10100187
Journal Name:
International Conference on Artificial Intelligence and Statistics
Sponsoring Org:
National Science Foundation
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