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Using Nonlinear Dimensionality Reduction to Visualize Classifiers

  • Alexander Schulz
  • Andrej Gisbrecht
  • Barbara Hammer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7902)

Abstract

Nonlinear dimensionality reduction (DR) techniques offer the possibility to visually inspect a given finite high-dimensional data set in two dimensions. In this contribution, we address the problem to visualize a trained classifier on top of these projections. We investigate the suitability of popular DR techniques for this purpose and we point out the benefit of integrating auxiliary information as provided by the classifier into the pipeline based on the Fisher information.

Keywords

Visualization of Classifiers Supervised Dimensionality Reduction Fisher Information 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alexander Schulz
    • 1
  • Andrej Gisbrecht
    • 1
  • Barbara Hammer
    • 1
  1. 1.CITEC Centre of ExcellenceUniversity of BielefeldGermany

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