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.
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Notes
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The gradient descent consistently converges with the learning rate \(\gamma =0.01\) in all our experiments; we did not observe further tuning of the learning rate to be necessary.
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Acknowledgments
This project was supported by Business Finland (project 3662/31/2018 Advanced Machine Learning for Industrial Applications) and the Academy of Finland (project 311277 TensorML).
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Jääsaari, E., Hyvönen, V., Roos, T. (2019). Efficient Autotuning of Hyperparameters in Approximate Nearest Neighbor Search. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11440. Springer, Cham. https://doi.org/10.1007/978-3-030-16145-3_46
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DOI: https://doi.org/10.1007/978-3-030-16145-3_46
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