Approximate nearest neighbor search for low-dimensional queries


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