Feature-Based Dissimilarity Space Classification

  • Robert P. W. Duin
  • Marco Loog
  • Elżbieta Pȩkalska
  • David M. J. Tax
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6388)


General dissimilarity-based learning approaches have been proposed for dissimilarity data sets [1,2]. They often arise in problems in which direct comparisons of objects are made by computing pairwise distances between images, spectra, graphs or strings.

Dissimilarity-based classifiers can also be defined in vector spaces [3]. A large comparative study has not been undertaken so far. This paper compares dissimilarity-based classifiers with traditional feature-based classifiers, including linear and nonlinear SVMs, in the context of the ICPR 2010 Classifier Domains of Competence contest. It is concluded that the feature-based dissimilarity space classification performs similar or better than the linear and nonlinear SVMs, as averaged over all 301 datasets of the contest and in a large subset of its datasets. This indicates that these classifiers have their own domain of competence.


Feature Space Dissimilarity Measure Linear Support Vector Machine Training Object Neighbor Rule 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pękalska, E., Duin, R.: The Dissimilarity Representation for Pattern Recognition. Foundations and Applications. World Scientific, Singapore (2005)CrossRefzbMATHGoogle Scholar
  2. 2.
    Pękalska, E., Duin, R.: Beyond traditional kernels: Classification in two dissimilarity-based representation spaces. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 38(6), 729–744 (2008)CrossRefGoogle Scholar
  3. 3.
    Pękalska, E., Duin, R.P.W.: Dissimilarity-based classification for vectorial representations. In: ICPR, vol. (3), pp. 137–140 (2006)Google Scholar
  4. 4.
    Edelman, S.: Representation and Recognition in Vision. MIT Press, Cambridge (1999)Google Scholar
  5. 5.
    Riesen, K., Bunke, H.: Graph classification based on vector space embedding. IJPRAI 23(6) (2009)Google Scholar
  6. 6.
    Xiao, B., Hancock, E.R., Wilson, R.C.: Geometric characterization and clustering of graphs using heat kernel embeddings. Image Vision Comput. 28(6), 1003–1021 (2010)CrossRefGoogle Scholar
  7. 7.
    Fischer, A., Riesen, K., Bunke, H.: An experimental study of graph classification using prototype selection. In: ICPR, pp. 1–4 (2008)Google Scholar
  8. 8.
    Pękalska, E., Duin, R., Paclík, P.: Prototype selection for dissimilarity-based classifiers. Pattern Recognition 39(2), 189–208 (2006)CrossRefzbMATHGoogle Scholar
  9. 9.
    Jacobs, D., Weinshall, D., Gdalyahu, Y.: Classification with Non-Metric Distances: Image Retrieval and Class Representation. IEEE TPAMI 22(6), 583–600 (2000)CrossRefGoogle Scholar
  10. 10.
    Duin, R., de Ridder, D., Tax, D.: Experiments with object based discriminant functions; a featureless approach to pattern recognition. Pattern Recognition Letters 18(11-13), 1159–1166 (1997)CrossRefGoogle Scholar
  11. 11.
    Graepel, T., Herbrich, R., Bollmann-Sdorra, P., Obermayer, K.: Classification on pairwise proximity data. In: Advances in Neural Information System Processing, vol. 11, pp. 438–444 (1999)Google Scholar
  12. 12.
    Pękalska, E., Paclík, P., Duin, R.: A Generalized Kernel Approach to Dissimilarity Based Classification. J. of Machine Learning Research 2(2), 175–211 (2002)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Duin, R., Juszczak, P., de Ridder, D., Paclík, P., Pękalska, E., Tax, D.: PR-Tools (2004),
  14. 14.
    Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Software available at

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Robert P. W. Duin
    • 1
  • Marco Loog
    • 1
  • Elżbieta Pȩkalska
    • 2
  • David M. J. Tax
    • 1
  1. 1.Faculty of Electrical Engineering, Mathematics and Computer SciencesDelft University of TechnologyThe Netherlands
  2. 2.School of Computer ScienceUniversity of ManchesterUnited Kingdom

Personalised recommendations