Discriminative Dimensionality Reduction Mappings

  • Andrej Gisbrecht
  • Daniela Hofmann
  • Barbara Hammer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7619)


Discriminative dimensionality reduction aims at a low dimensional, usually nonlinear representation of given data such that information as specified by auxiliary discriminative labeling is presented as accurately as possible. This paper centers around two open problems connected to this question: (i) how to evaluate discriminative dimensionality reduction quantitatively? (ii) how to arrive at explicit nonlinear discriminative dimensionality reduction mappings? Based on recent work for the unsupervised case, we propose an evaluation measure and an explicit discriminative dimensionality reduction mapping using the Fisher information.


Dimensionality Reduction Linear Discriminant Analysis Class Label Fisher Information Fisher Information Matrix 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andrej Gisbrecht
    • 1
  • Daniela Hofmann
    • 1
  • Barbara Hammer
    • 1
  1. 1.CITEC Centre of ExcellenceUniversity of BielefeldGermany

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