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Approximate search in dissimilarity spaces using GA

Authors:

David Bernhauer,

Tomáš SkopalAuthors Info & Claims

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 279 - 280

https://doi.org/10.1145/3319619.3321907

Published: 13 July 2019 Publication History

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Abstract

Nowadays, the metric space properties limit the methods of indexing for content-based similarity search. The target of this paper is a data-driven transformation of a semimetric model to a metric one while keeping the data indexability high. We have proposed a genetic algorithm for evolutionary design of semimetric-to-metric modifiers. The precision of our algorithm is near the specified error threshold and indexability is still good. The paper contribution is a proof of concept showing that genetic algorithms can effectively design semimetric modifiers applicable in similarity search engines.

References

[1]

Tomáš Bartoš, Tomáš Skopal, and Juraj Moško. 2013. Efficient Indexing of Similarity Models with Inequality Symbolic Regression. In Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation (GECCO '13). ACM, New York, NY, USA, 901--908.

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[2]

Ronald Fagin and Larry Stockmeyer. 1998. Relaxing the Triangle Inequality in Pattern Matching. International Journal of Computer Vision 30, 3 (01 Dec 1998), 219--231.

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Magnus Lie Hetland, Tomáš Skopal, Jakub Lokoč, and Christian Beecks. 2013. Ptolemaic access methods: Challenging the reign of the metric space model. Information Systems 38, 7 (2013), 989 -- 1006.

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[4]

Francisco Moreno, Luisa Mico, and Jose Oncina. 2002. Extending LAESA Fast Nearest Neighbour Algorithm to Find the k Nearest Neighbours. 718--724.

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[5]

Tomáš Skopal. 2007. Unified Framework for Fast Exact and Approximate Search in Dissimilarity Spaces. ACM Trans. Database Syst. 32, 4, Article 29 (Nov. 2007).

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Cited By

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Rezazadeh Hamedani AMoattar MForghani Y(2021)Dissimilarity space reinforced with manifold learning and latent space modeling for improved pattern classificationJournal of Big Data10.1186/s40537-021-00527-68:1Online publication date: 18-Oct-2021
https://doi.org/10.1186/s40537-021-00527-6
Bernhauer DSkopal T(2020)Analysing Indexability of Intrinsically High-Dimensional Data Using TriGenSimilarity Search and Applications10.1007/978-3-030-60936-8_20(261-269)Online publication date: 14-Oct-2020
https://doi.org/10.1007/978-3-030-60936-8_20
Bernhauer DSkopal T(2019)Non-metric Similarity Search Using Genetic TriGenSimilarity Search and Applications10.1007/978-3-030-32047-8_8(86-93)Online publication date: 23-Sep-2019
https://doi.org/10.1007/978-3-030-32047-8_8

Index Terms

Approximate search in dissimilarity spaces using GA
1. Information systems
  1. Information retrieval
    1. Search engine architectures and scalability
      1. Search engine indexing

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Information

Published In

GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2019

2161 pages

ISBN:9781450367486

DOI:10.1145/3319619

Editor:
Manuel López-Ibáñez
University of Manchester, UK
,
General Chairs:
Anne Auger
Inria and Ecole Polytechnique, France
,
Thomas Stützle
IRIDIA, Université libre de Bruxelles Belgium

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2019

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GECCO '19

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SIGEVO

GECCO '19: Genetic and Evolutionary Computation Conference

July 13 - 17, 2019

Prague, Czech Republic

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Rezazadeh Hamedani AMoattar MForghani Y(2021)Dissimilarity space reinforced with manifold learning and latent space modeling for improved pattern classificationJournal of Big Data10.1186/s40537-021-00527-68:1Online publication date: 18-Oct-2021
https://doi.org/10.1186/s40537-021-00527-6
Bernhauer DSkopal T(2020)Analysing Indexability of Intrinsically High-Dimensional Data Using TriGenSimilarity Search and Applications10.1007/978-3-030-60936-8_20(261-269)Online publication date: 14-Oct-2020
https://doi.org/10.1007/978-3-030-60936-8_20
Bernhauer DSkopal T(2019)Non-metric Similarity Search Using Genetic TriGenSimilarity Search and Applications10.1007/978-3-030-32047-8_8(86-93)Online publication date: 23-Sep-2019
https://doi.org/10.1007/978-3-030-32047-8_8

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On Scalable Approximate Search with the Signature Quadratic Form Distance

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Efficient approximate nearest neighbor search with integrated binary codes