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Visualization by Linear Projections as Information Retrieval

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

Abstract

We apply a recent formalization of visualization as information retrieval to linear projections. We introduce a method that optimizes a linear projection for an information retrieval task: retrieving neighbors of input samples based on their low-dimensional visualization coordinates only. The simple linear projection makes the method easy to interpret, while the visualization task is made well-defined by the novel information retrieval criterion. The method has a further advantage: it projects input features, but the input neighborhoods it preserves can be given separately from the input features, e.g. by external data of sample similarities. Thus the visualization can reveal the relationship between data features and complicated data similarities. We further extend the method to kernel-based projections.

Keywords

visualization information retrieval linear projection 

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References

  1. 1.
    Cevikalp, H., Verbeek, J., Jurie, F., Kläser, A.: Semi-supervised dimensionality reduction using pairwise equivalence constraints. In: Proc. VISAPP 2008, pp. 489–496 (2008)Google Scholar
  2. 2.
    Peltonen, J., Kaski, S.: Discriminative components of data. IEEE Trans. Neural Networks 16(1), 68–83 (2005)CrossRefGoogle Scholar
  3. 3.
    Goldberger, J., Roweis, S., Hinton, G., Salakhutdinov, R.: Neighbourhood components analysis. In: Proc. NIPS 2004, pp. 513–520. MIT Press, Cambridge (2005)Google Scholar
  4. 4.
    Globerson, A., Roweis, S.: Metric learning by collapsing classes. In: Proc. NIPS 2005, pp. 451–458. MIT Press, Cambridge (2006)Google Scholar
  5. 5.
    Venna, J., Kaski, S.: Nonlinear dimensionality reduction as information retrieval. In: Proc. AISTATS 2007 (2007)Google Scholar
  6. 6.
    Peltonen, J., Aidos, H., Kaski, S.: Supervised nonlinear dimensionality reduction by neighbor retrieval. In: Proc. ICASSP 2009 (in press, 2009)Google Scholar
  7. 7.
    Hinton, G., Roweis, S.T.: Stochastic neighbor embedding. In: Proc. NIPS 2002, pp. 833–840. MIT Press, Cambridge (2002)Google Scholar
  8. 8.
    Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290 (December 2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jaakko Peltonen
    • 1
  1. 1.Department of Information and Computer ScienceHelsinki University of TechnologyTKKFinland

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