Approximate Nearest Neighbor Search for Low Dimensional Queries

TL;DR

This paper addresses efficient approximate nearest neighbor search when queries are low-dimensional, but data is high-dimensional, providing solutions that leverage the low doubling dimension of query space.

Contribution

It introduces methods for efficient approximate nearest neighbor search in high-dimensional data with low-dimensional query constraints, exploiting low doubling dimension.

Findings

Efficient algorithms for low doubling dimension query spaces.

Performance remains robust despite high data dimensionality.

The approach improves search speed in relevant applications.

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.