# Efficient Autotuning of Hyperparameters in Approximate Nearest Neighbor Search

## TL;DR

This paper introduces an automatic hyperparameter tuning algorithm for randomized space-partitioning trees in approximate nearest neighbor search, significantly reducing tuning time while maintaining competitive accuracy and speed.

## Contribution

It presents a novel, fast hyperparameter tuning method for randomized trees that improves over grid search and enhances indexing efficiency.

## Key findings

- Tuning algorithm finds optimal hyperparameters with minimal overhead.
- The method is significantly faster than existing tuning approaches.
- Indexing methods remain competitive in query time and are faster to build.

## Abstract

Approximate nearest neighbor algorithms are used to speed up nearest neighbor search in a wide array of applications. However, current indexing methods feature several hyperparameters that need to be tuned to reach an acceptable accuracy--speed trade-off. A grid search in the parameter space is often impractically slow due to a time-consuming index-building procedure. Therefore, we propose an algorithm for automatically tuning the hyperparameters of indexing methods based on randomized space-partitioning trees. In particular, we present results using randomized k-d trees, random projection trees and randomized PCA trees. The tuning algorithm adds minimal overhead to the index-building process but is able to find the optimal hyperparameters accurately. We demonstrate that the algorithm is significantly faster than existing approaches, and that the indexing methods used are competitive with the state-of-the-art methods in query time while being faster to build.