Efficient Autotuning of Hyperparameters in Approximate Nearest Neighbor Search
This work addresses the hyperparameter tuning bottleneck for practitioners using approximate nearest neighbor search in applications like recommendation systems or image retrieval, though it is incremental as it builds on existing indexing methods.
The paper tackles the problem of efficiently tuning hyperparameters in approximate nearest neighbor search algorithms, which is slow with grid search due to time-consuming index-building, and proposes an autotuning algorithm that adds minimal overhead, achieving significantly faster tuning while maintaining competitive query performance.
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.