Approximate nearest neighbor search for low-dimensional queries

摘要

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.