License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
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DOI: 10.4230/LIPIcs.SEA.2020.1
URN: urn:nbn:de:0030-drops-120751
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Aumüller, Martin

Algorithm Engineering for High-Dimensional Similarity Search Problems (Invited Talk)

LIPIcs-SEA-2020-1.pdf (0.3 MB)


Similarity search problems in high-dimensional data arise in many areas of computer science such as data bases, image analysis, machine learning, and natural language processing. One of the most prominent problems is finding the k nearest neighbors of a data point q ∈ ℝ^d in a large set of data points S ⊂ ℝ^d, under same distance measure such as Euclidean distance. In contrast to lower dimensional settings, we do not know of worst-case efficient data structures for such search problems in high-dimensional data, i.e., data structures that are faster than a linear scan through the data set. However, there is a rich body of (often heuristic) approaches that solve nearest neighbor search problems much faster than such a scan on many real-world data sets. As a necessity, the term solve means that these approaches give approximate results that are close to the true k-nearest neighbors. In this talk, we survey recent approaches to nearest neighbor search and related problems.
The talk consists of three parts: (1) What makes nearest neighbor search difficult? (2) How do current state-of-the-art algorithms work? (3) What are recent advances regarding similarity search on GPUs, in distributed settings, or in external memory?

BibTeX - Entry

  author =	{Martin Aum{\"u}ller},
  title =	{{Algorithm Engineering for High-Dimensional Similarity Search Problems (Invited Talk)}},
  booktitle =	{18th International Symposium on Experimental Algorithms (SEA 2020)},
  pages =	{1:1--1:3},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-148-1},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{160},
  editor =	{Simone Faro and Domenico Cantone},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-120751},
  doi =		{10.4230/LIPIcs.SEA.2020.1},
  annote =	{Keywords: Nearest neighbor search, Benchmarking}

Keywords: Nearest neighbor search, Benchmarking
Collection: 18th International Symposium on Experimental Algorithms (SEA 2020)
Issue Date: 2020
Date of publication: 12.06.2020

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