Approximate Nearest Neighbor Search for Low Dimensional Queries (1007.3296v3)

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.