Non-Euclidean Dissimilarities: Causes and Informativeness

  • Robert P. W. Duin
  • Elżbieta Pękalska
Conference paper

DOI: 10.1007/978-3-642-14980-1_31

Volume 6218 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Duin R.P.W., Pękalska E. (2010) Non-Euclidean Dissimilarities: Causes and Informativeness. In: Hancock E.R., Wilson R.C., Windeatt T., Ulusoy I., Escolano F. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2010. Lecture Notes in Computer Science, vol 6218. Springer, Berlin, Heidelberg

Abstract

In the process of designing pattern recognition systems one may choose a representation based on pairwise dissimilarities between objects. This is especially appealing when a set of discriminative features is difficult to find. Various classification systems have been studied for such a dissimilarity representation: the direct use of the nearest neighbor rule, the postulation of a dissimilarity space and an embedding to a virtual, underlying feature vector space.

It appears in several applications that the dissimilarity measures constructed by experts tend to have a non-Euclidean behavior. In this paper we first analyze the causes of such choices and then experimentally verify that the non-Euclidean property of the measure can be informative.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Robert P. W. Duin
    • 1
  • Elżbieta Pękalska
    • 2
  1. 1.Faculty of Electrical Engineering, Mathematics and Computer SciencesDelft University of TechnologyThe Netherlands
  2. 2.School of Computer ScienceUniversity of ManchesterUnited Kingdom