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The Role of Local Intrinsic Dimensionality in Benchmarking Nearest Neighbor Search

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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.
Original languageEnglish
Title of host publicationInternational Conference on Similarity Search and Applications : SISAP 2019: Similarity Search and Applications
PublisherSpringer
Publication date17 Jul 2019
ISBN (Print)978-3-030-32046-1
ISBN (Electronic)978-3-030-32047-8
DOIs
Publication statusPublished - 17 Jul 2019
SeriesLecture Notes in Computer Science
Volume11807
ISSN0302-9743

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