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.


Template Match Dissimilarity Measure Dissimilarity Matrix Neighbor Rule Dissimilarity Matrice 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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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

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