Schloss Dagstuhl - Leibniz-Zentrum fΓΌr Informatik GmbH Schloss Dagstuhl - Leibniz-Zentrum fΓΌr Informatik GmbH scholarly article en Kush, Deepanshu; Nikolov, Aleksandar; Tang, Haohua https://www.dagstuhl.de/lipics License: Creative Commons Attribution 4.0 license (CC BY 4.0)
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URN: urn:nbn:de:0030-drops-138490
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Near Neighbor Search via Efficient Average Distortion Embeddings

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Abstract

A recent series of papers by Andoni, Naor, Nikolov, Razenshteyn, and Waingarten (STOC 2018, FOCS 2018) has given approximate near neighbour search (NNS) data structures for a wide class of distance metrics, including all norms. In particular, these data structures achieve approximation on the order of p for 𝓁_p^d norms with space complexity nearly linear in the dataset size n and polynomial in the dimension d, and query time sub-linear in n and polynomial in d. The main shortcoming is the exponential in d pre-processing time required for their construction.
In this paper, we describe a more direct framework for constructing NNS data structures for general norms. More specifically, we show via an algorithmic reduction that an efficient NNS data structure for a metric β„³ is implied by an efficient average distortion embedding of β„³ into 𝓁₁ or the Euclidean space. In particular, the resulting data structures require only polynomial pre-processing time, as long as the embedding can be computed in polynomial time.
As a concrete instantiation of this framework, we give an NNS data structure for 𝓁_p with efficient pre-processing that matches the approximation factor, space and query complexity of the aforementioned data structure of Andoni et al. On the way, we resolve a question of Naor (Analysis and Geometry in Metric Spaces, 2014) and provide an explicit, efficiently computable embedding of 𝓁_p, for p β‰₯ 1, into 𝓁₁ with average distortion on the order of p. Furthermore, we also give data structures for Schatten-p spaces with improved space and query complexity, albeit still requiring exponential pre-processing when p β‰₯ 2. We expect our approach to pave the way for constructing efficient NNS data structures for all norms.

BibTeX - Entry

@InProceedings{kush_et_al:LIPIcs.SoCG.2021.50,
  author =	{Kush, Deepanshu and Nikolov, Aleksandar and Tang, Haohua},
  title =	{{Near Neighbor Search via Efficient Average Distortion Embeddings}},
  booktitle =	{37th International Symposium on Computational Geometry (SoCG 2021)},
  pages =	{50:1--50:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-184-9},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{189},
  editor =	{Buchin, Kevin and Colin de Verdi\`{e}re, \'{E}ric},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2021/13849},
  URN =		{urn:nbn:de:0030-drops-138490},
  doi =		{10.4230/LIPIcs.SoCG.2021.50},
  annote =	{Keywords: Nearest neighbor search, metric space embeddings, average distortion embeddings, locality-sensitive hashing}
}

Keywords: Nearest neighbor search, metric space embeddings, average distortion embeddings, locality-sensitive hashing
Seminar: 37th International Symposium on Computational Geometry (SoCG 2021)
Issue date: 2021
Date of publication: 02.06.2021


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