Efficient Autotuning of Hyperparameters in Approximate Nearest Neighbor Search

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http://hdl.handle.net/10138/303722

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Jääsaari , E , Hyvönen , V & Roos , T 2019 , Efficient Autotuning of Hyperparameters in Approximate Nearest Neighbor Search . in Advances in Knowledge Discovery and Data Mining : 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part II . Lecture Notes in Artificial Intelligence , vol. 11440 , Springer , Cham , pp. 590-602 , Pacific-Asia Conference on Knowledge Discovery and Data Mining 2019 , Macao , China , 14/04/2019 . https://doi.org/10.1007/978-3-030-16145-3_46

Title: Efficient Autotuning of Hyperparameters in Approximate Nearest Neighbor Search
Author: Jääsaari, Elias; Hyvönen, Ville; Roos, Teemu
Contributor: University of Helsinki, Kvasir Ltd.
University of Helsinki, Department of Computer Science
University of Helsinki, Information, Complexity and Learning research group / Teemu Roos
Publisher: Springer
Date: 2019-04-17
Language: eng
Number of pages: 13
Belongs to series: Advances in Knowledge Discovery and Data Mining 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part II
Belongs to series: Lecture Notes in Artificial Intelligence
ISBN: 978-3-030-16144-6
978-3-030-16145-3
URI: http://hdl.handle.net/10138/303722
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.
Subject: 113 Computer and information sciences
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