Approximate Nearest Neighbors in Limited Space

Abstract

We consider the $(1+ε)$-approximate nearest neighbor search problem: given a set $X$ of $n$ points in a $d$-dimensional space, build a data structure that, given any query point $y$, finds a point $x \in X$ whose distance to $y$ is at most $(1+ε) \min_{x \in X} \|x-y\|$ for an accuracy parameter $ε\in (0,1)$. Our main result is a data structure that occupies only $O(ε^{-2} n \log(n) \log(1/ε))$ bits of space, assuming all point coordinates are integers in the range $\{-n^{O(1)} \ldots n^{O(1)}\}$, i.e., the coordinates have $O(\log n)$ bits of precision. This improves over the best previously known space bound of $O(ε^{-2} n \log(n)^2)$, obtained via the randomized dimensionality reduction method of Johnson and Lindenstrauss (1984). We also consider the more general problem of estimating all distances from a collection of query points to all data points $X$, and provide almost tight upper and lower bounds for the space complexity of this problem.

COLT 2018