Efficient Autotuning of Hyperparameters in Approximate Nearest Neighbor Search

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dc.contributor.author Jääsaari, Elias
dc.contributor.author Hyvönen, Ville
dc.contributor.author Roos, Teemu
dc.date.accessioned 2019-07-02T11:38:01Z
dc.date.available 2019-07-02T11:38:01Z
dc.date.issued 2019-04-17
dc.identifier.citation 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
dc.identifier.citation conference
dc.identifier.other PURE: 124062124
dc.identifier.other PURE UUID: c41fb03e-bca2-4cc3-9715-80437e32ba4d
dc.identifier.other ORCID: /0000-0001-9470-3759/work/59203454
dc.identifier.other WOS: 000716968700046
dc.identifier.uri http://hdl.handle.net/10138/303722
dc.description.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. fi
dc.format.extent 13
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartof Advances in Knowledge Discovery and Data Mining
dc.relation.ispartofseries Lecture Notes in Artificial Intelligence
dc.relation.isversionof 978-3-030-16144-6
dc.relation.isversionof 978-3-030-16145-3
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 113 Computer and information sciences
dc.title Efficient Autotuning of Hyperparameters in Approximate Nearest Neighbor Search en
dc.type Conference contribution
dc.contributor.organization Department of Computer Science
dc.contributor.organization Helsinki Institute for Information Technology
dc.contributor.organization Information, Complexity and Learning research group / Teemu Roos
dc.contributor.organization Complex Systems Computation Group
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1007/978-3-030-16145-3_46
dc.relation.issn 0302-9743
dc.rights.accesslevel openAccess
dc.type.version acceptedVersion

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