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Extending Fast Nearest Neighbour Search Algorithms for Approximate k-NN Classification

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Pattern Recognition and Image Analysis (IbPRIA 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2652))

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Abstract

The nearest neighbour (NN) and k-nearest neighbour (k-NN) classification rules have been widely used in pattern recognition due to its simplicity and good behaviour. Exhaustive nearest neighbour search can become unpractical when facing large training sets, high dimensional data or expensive similarity measures. In the last years a lot of NN search algorithms have been developed to overcome those problems, and many of them are based on traversing a data structure (usually a tree) and selecting several candidates until the nearest neighbour is found. In this paper we propose a new classification rule that makes use of those selected (and usually discarded) prototypes. Several fast and widely known NN search algorithms have been extended with this rule obtaining classification results similar to those of a k-NN classifier without extra computational overhead.

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Author information

Authors and Affiliations

  1. Dept. Lenguajes y Sistemas Informáticos, Universdad de Alicante, E-03071, Alicante, Spain

    Francisco Moreno-Seco, Luisa Micó & Jose Oncina

Authors
  1. Francisco Moreno-Seco
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  2. Luisa Micó
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  3. Jose Oncina
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Editor information

Editors and Affiliations

  1. Unitat de Gràfics i Visió per Ordinador Departament de Ciències Matemàtiques i Informàtica, Universitat de les Illes Balears Edifici Anselm Turmeda, Ctra. de Valldemossa km 7,5, 07122, Palma de Mallorca, Spain

    Francisco José Perales

  2. FEUP - Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio J. C. Campilho

  3. Departamento de Ciencias da la Computacíon e I.A., Universidad de Granada, E.T. S. Ing. Informática, 18071, Granada, Spain

    Nicolás Pérez de la Blanca

  4. Dept. System Engineering and Automation, Universitat Politècnica de Catalunya (UPC) Barcelona, Spain

    Alberto Sanfeliu

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© 2003 Springer-Verlag Berlin Heidelberg

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Moreno-Seco, F., Micó, L., Oncina, J. (2003). Extending Fast Nearest Neighbour Search Algorithms for Approximate k-NN Classification. In: Perales, F.J., Campilho, A.J.C., de la Blanca, N.P., Sanfeliu, A. (eds) Pattern Recognition and Image Analysis. IbPRIA 2003. Lecture Notes in Computer Science, vol 2652. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44871-6_69

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  • DOI: https://doi.org/10.1007/978-3-540-44871-6_69

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40217-6

  • Online ISBN: 978-3-540-44871-6

  • eBook Packages: Springer Book Archive

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