Approximate nearest neighbor search for low-dimensional queries
Abstract
We study the approximate nearest neighbor problem for metric spaces where the query points are constrained to lie on a subspace of low doubling dimension, while the data is high dimensional. We show that this problem can be solved efficiently despite the high dimensionality of the data. © 2013 Society for Industrial and Applied Mathematics.
Publication Title
SIAM Journal on Computing
Recommended Citation
Har-Peled, S., & Kumar, N. (2013). Approximate nearest neighbor search for low-dimensional queries. SIAM Journal on Computing, 42 (1), 138-159. https://doi.org/10.1137/110852711
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