Approximate Nearest Neighbor Search in High Dimensions

TL;DR

This paper surveys the development of approximate nearest neighbor search algorithms in high-dimensional spaces, highlighting their applications and connections to geometric analysis over the past two decades.

Contribution

It provides a comprehensive overview of existing methods for approximate nearest neighbor search and explores their theoretical foundations and practical implications.

Findings

Summarizes key algorithms and techniques for approximate nearest neighbor search.

Highlights connections between algorithmic solutions and geometric functional analysis.

Discusses applications in machine learning, computer vision, and databases.

Abstract

The nearest neighbor problem is defined as follows: Given a set $P$ of $n$ points in some metric space $(X,D)$, build a data structure that, given any point $q$, returns a point in $P$ that is closest to $q$ (its "nearest neighbor" in $P$). The data structure stores additional information about the set $P$, which is then used to find the nearest neighbor without computing all distances between $q$ and $P$. The problem has a wide range of applications in machine learning, computer vision, databases and other fields. To reduce the time needed to find nearest neighbors and the amount of memory used by the data structure, one can formulate the {\em approximate} nearest neighbor problem, where the the goal is to return any point $p' \in P$ such that the distance from $q$ to $p'$ is at most $c \cdot \min_{p \in P} D(q,p)$, for some $c \geq 1$. Over the last two decades, many efficient solutions to this problem were developed. In this article we survey these developments, as well as their connections to questions in geometric functional analysis and combinatorial geometry.