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Man-Machine Interactions 3 pp 3–15Cite as

Pattern Recognition with Non-Euclidean Similarities

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 242))

Abstract

Pairwise dissimilarity representations are frequently used as an alternative to feature vectors in pattern recognition.One of the problems encountered in the analysis of such data, is that the dissimilarities are rarely Euclidean, while statistical learning algorithms often rely on Euclidean distances. Such non-Euclidean dissimilarities are often corrected or imposed geometry via embedding. This talk reviews and and extends the field of analysing non-Euclidean dissimilarity data.

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Authors and Affiliations

  1. Department of Computer Science, University of York, York, United Kingdom

    Edwin R. Hancock, Eliza Xu & Richard C. Wilson

Authors
  1. Edwin R. Hancock
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  2. Eliza Xu
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  3. Richard C. Wilson
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Corresponding author

Correspondence to Edwin R. Hancock .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dr. Aleksandra Gruca

  2. Polish Academy of Sciences and Silesian University of Technology, Gliwice, Poland

    Tadeusz Czachórski

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

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Hancock, E.R., Xu, E., Wilson, R.C. (2014). Pattern Recognition with Non-Euclidean Similarities. In: Gruca, D., Czachórski, T., Kozielski, S. (eds) Man-Machine Interactions 3. Advances in Intelligent Systems and Computing, vol 242. Springer, Cham. https://doi.org/10.1007/978-3-319-02309-0_1

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  • DOI: https://doi.org/10.1007/978-3-319-02309-0_1

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02308-3

  • Online ISBN: 978-3-319-02309-0

  • eBook Packages: EngineeringEngineering (R0)

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