The Exploration Machine – A Novel Method for Data Visualization

  • Axel Wismüller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5629)

Abstract

We present a novel method for structure-preserving dimensionality reduction. The Exploration Machine (Exploratory Observation Machine, XOM) computes graphical representations of high-dimensional observations by a strategy of self-organized model adaptation. Although simple and computationally efficient, XOM enjoys a surprising flexibility to simultaneously contribute to several different domains of advanced machine learning, scientific data analysis, and visualization, such as structure-preserving dimensionality reduction and data clustering.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cherry, J.M., Ball, C., Weng, S., Juvik, G., Schmidt, R., Adler, C., Dunn, B., Dwight, S., Riles, L., Mortimer, R.K.: Nature 387, 67–73 (1997)CrossRefGoogle Scholar
  2. 2.
    Eisen, M.B., Spellman, P.T., Brown, P.O., Botstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868 (1998)CrossRefGoogle Scholar
  3. 3.
    Graepel, T., Burger, M., Obermayer, K.: Self-organizing maps: Generalizations and new optimization techniques. Neurocomputing 21, 173–190 (1998)CrossRefMATHGoogle Scholar
  4. 4.
    Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Heidelberg (2001)CrossRefMATHGoogle Scholar
  5. 5.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  6. 6.
    Sammon, J.W.: A nonlinear mapping for data structure analysis. IEEE Transactions on Computers C 18, 401–409 (1969)CrossRefGoogle Scholar
  7. 7.
    Tenenbaum, J.B., de Silva, V., Langford, C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)CrossRefGoogle Scholar
  8. 8.
    Ultsch, A.: Maps for the visualization of high-dimensional data spaces. In: Proceedings of the Workshop on Self-Organizing Maps 2003 (WSOM 2003), Hibikino, Kitakyushu, Japan, pp. 225–230 (2003)Google Scholar
  9. 9.
    Wismüller, A.: Exploratory Morphogenesis (XOM): A Novel Computational Framework for Self-Organization. Ph.D. thesis, Technical University of Munich, Department of Electrical and Computer Engineering (2006)Google Scholar
  10. 10.
    Wismüller, A., Lange, O., Dersch, D.R., Leinsinger, G.L., Hahn, K., Pütz, B., Auer, D.: Cluster analysis of biomedical image time-series. International Journal of Computer Vision 46(2), 103–128 (2002)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Axel Wismüller
    • 1
  1. 1.Depts. of Radiology and Biomedical EngineeringUniversity of RochesterNew YorkU.S.A.

Personalised recommendations