The Dissimilarity Representation as a Tool for Three-Way Data Classification: A 2D Measure

  • Diana Porro-Muñoz
  • Robert P. W. Duin
  • Mauricio Orozco-Alzate
  • Isneri Talavera
  • John Makario Londoño-Bonilla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6218)

Abstract

The dissimilarity representation has demonstrated advantages in the solution of classification problems. Meanwhile, the representation of objects by multi-dimensional arrays is necessary in many research areas. However, the development of proper classification tools that take the multi-way structure into account is incipient. This paper introduces the use of the dissimilarity representation as a tool for classifying three-way data, as dissimilarities allow the representation of multi-dimensional objects in a natural way. As an example, the classification of three-way seismic volcanic data is used. A comparison is made between dissimilarity measures used in different representations of the three-way data. 2D dissimilarity measures for three-way data can be useful.

Keywords

Object representation classification multi-dimensional data dissimilarity representation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Diana Porro-Muñoz
    • 1
    • 2
  • Robert P. W. Duin
    • 2
  • Mauricio Orozco-Alzate
    • 3
  • Isneri Talavera
    • 1
  • John Makario Londoño-Bonilla
    • 4
  1. 1.Advanced Technologies Application Center (Cenatav)Cuba
  2. 2.Pattern Recognition LabTU DelftThe Netherlands
  3. 3.Universidad Nacional de Colombia Sede ManizalesColombia
  4. 4.Instituto Colombiano de Geología y Minería (Ingeominas)Colombia

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