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Fast Approximate Furthest Neighbors with Data-Dependent Candidate Selection

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Similarity Search and Applications (SISAP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9939))

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Abstract

We present a novel strategy for approximate furthest neighbor search that selects a candidate set using the data distribution. This strategy leads to an algorithm, which we call DrusillaSelect, that is able to outperform existing approximate furthest neighbor strategies. Our strategy is motivated by an empirical study of the behavior of the furthest neighbor search problem, which lends intuition for where our algorithm is most useful. We also present a variant of the algorithm that gives an absolute approximation guarantee; under some assumptions, the guaranteed approximation can be achieved in provably less time than brute-force search. Performance studies indicate that DrusillaSelect can achieve comparable levels of approximation to other algorithms while giving up to an order of magnitude speedup. An implementation is available in the mlpack machine learning library (found at http://www.mlpack.org).

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Notes

  1. 1.

    This is where the algorithm gets its name; the first author’s cat displays the same behavior when selecting a food bowl to eat from.

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Authors and Affiliations

  1. Center for Advanced Machine Learning, Symantec Corporation, Atlanta, Georgia, 30338, USA

    Ryan R. Curtin & Andrew B. Gardner

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  1. Ryan R. Curtin
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  2. Andrew B. Gardner
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Correspondence to Ryan R. Curtin .

Editor information

Editors and Affiliations

  1. CNRS–IRISA , Rennes, France

    Laurent Amsaleg

  2. National Institute of Informatics , Tokyo, Japan

    Michael E. Houle

  3. Ludwig-Maximilians-Universität München , München, Germany

    Erich Schubert

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Curtin, R.R., Gardner, A.B. (2016). Fast Approximate Furthest Neighbors with Data-Dependent Candidate Selection. In: Amsaleg, L., Houle, M., Schubert, E. (eds) Similarity Search and Applications. SISAP 2016. Lecture Notes in Computer Science(), vol 9939. Springer, Cham. https://doi.org/10.1007/978-3-319-46759-7_17

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  • DOI: https://doi.org/10.1007/978-3-319-46759-7_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46758-0

  • Online ISBN: 978-3-319-46759-7

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