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

Author Martin Aumüller

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Martin Aumüller
  • IT University of Copenhagen, Denmark

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Martin Aumüller. Algorithm Engineering for High-Dimensional Similarity Search Problems (Invited Talk). In 18th International Symposium on Experimental Algorithms (SEA 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 160, pp. 1:1-1:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/LIPIcs.SEA.2020.1

Abstract

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?

Subject Classification

ACM Subject Classification
  • Theory of computation → Nearest neighbor algorithms
  • Computing methodologies → Simulation evaluation
  • Theory of computation → Computational geometry
Keywords
  • Nearest neighbor search
  • Benchmarking

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References

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