Structural, Syntactic, and Statistical Pattern Recognition

Volume 6218 of the series Lecture Notes in Computer Science pp 324-333

Non-Euclidean Dissimilarities: Causes and Informativeness

  • Robert P. W. DuinAffiliated withFaculty of Electrical Engineering, Mathematics and Computer Sciences, Delft University of Technology
  • , Elżbieta PękalskaAffiliated withSchool of Computer Science, University of Manchester

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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.