Visualizing Hierarchical Representation in a Multilayered Restricted RBF Network

  • Pitoyo Hartono
  • Paul Hollensen
  • Thomas Trappenberg
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)


In this study we propose a hierarchical neural network that is able to generate a topographical map in its internal layer. The map significantly differs from the conventional Kohonen’s SOM, in that it preserves the topological characteristics in relevance to the context, for example the labels, of the data. This map is useful if we are interested in visualizing the underlying characteristics of the classificability of the data that traditionally cannot be visualized with the standard SOM. In this paper, we expand our network into a multilayered structure that allows us visualize and thus better understand on how the neural network perceives the given data in the light of classification task.


Self-Organizing Map Supervised Learning Hierarchical Representation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hartono, P., Trappenberg, T.: Classificability-regulated Self-Organizing Map using Restricted RBF. In: Proc. IEEE Int. Joint Conference on Neural Networks (IJCNN 2013), pp. 160–164 (2013)Google Scholar
  2. 2.
    Poggio, T., Girosi, F.: Networks for Approximation and Learning. Proceedings of IEEE 78(9), 1484–1487 (1990)CrossRefGoogle Scholar
  3. 3.
    Kohonen, T.: Self-organized Formation of Topologically Correct Feature Maps. Biological Cybernetics 43, 59–69 (1982)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Neme, A., Miramontes, P.: Self-Organizing Map Formation with a Selectively Refractory Neighborhood. Neural Processing Letters 39, 1–24 (2014)CrossRefGoogle Scholar
  5. 5.
    Yin, H.: ViSOM-A Novel Method for Multivariate Data Projection and Structure Visualization. IEEE Trans. Neural Networks 13(1), 237–243 (2002)Google Scholar
  6. 6.
    Wu, S., Chow, T.W.S.: PRSOM: A New Visualization Method by Hybridizing Multidimensional Scaling and Self-Organizing Map. IEEE Trans. on Neural Networks 16(6), 1362–1380 (2005)Google Scholar
  7. 7.
    Bengio, Y.: Learning Deep Architecture for AI. Foundations and Trends in Machine Learning 2(1), 1–127 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-Based Learning Applied to Document Recognition. IEEE Proceedings 86(11), 2279–2324 (1998)CrossRefGoogle Scholar
  9. 9.
    van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. Journal of Machine Learning Research 9(85), 2579–2605 (2008)zbMATHGoogle Scholar
  10. 10.
    Rumelhart, D., McClelland, J.: Learning Internal Representation by Error Propagation. Parallel Distributed Processing, vol. 1, pp. 318–362. MIT Press (1984)Google Scholar
  11. 11.
    Willshaw, D.J., Von Der Malsburg, C.: How patterned neural connections can be set up by self-organization. Proc. Royal Society of London 194(1117), 431–445 (1976)CrossRefGoogle Scholar
  12. 12.
    Duin, R.P.W., et al.: PRTools4, A Matlab Toolbox for Pattern Recognition. Delft University of Technology (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Pitoyo Hartono
    • 1
  • Paul Hollensen
    • 2
  • Thomas Trappenberg
    • 2
  1. 1.School of EngineeringChukyo UniversityNagoyaJapan
  2. 2.School of Computer ScienceDalhousie UniversityHalifaxCanada

Personalised recommendations