The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search

Martin Aumüller, Matteo Ceccarello

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Abstrakt

This paper reconsiders common benchmarking approaches to nearest neighbor search. It is shown that the concept of local intrinsic dimensionality (LID) allows to choose query sets of a wide range of difficulty for real-world datasets. Moreover, the effect of different LID distributions on the running time performance of implementations is empirically studied. To this end, different visualization concepts are introduced that allow to get a more fine-grained overview of the inner workings of nearest neighbor search principles. The paper closes with remarks about the diversity of datasets commonly used for nearest neighbor search benchmarking. It is shown that such real-world datasets are not diverse: results on a single dataset predict results on all other datasets well.
OriginalsprogEngelsk
TitelInternational Conference on Similarity Search and Applications : SISAP 2019: Similarity Search and Applications
ForlagSpringer
Publikationsdato17 jul. 2019
ISBN (Trykt)978-3-030-32046-1
ISBN (Elektronisk)978-3-030-32047-8
DOI
StatusUdgivet - 17 jul. 2019
NavnLecture Notes in Computer Science
Vol/bind11807
ISSN0302-9743

Emneord

  • cs.IR
  • cs.DB

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