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Coordinating Principal Component Analyzers

  • Jakob J. Verbeek
  • Nikos Vlassis
  • Ben Kröse
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2415)

Abstract

Mixtures of Principal Component Analyzers can be used to model high dimensional data that lie on or near a low dimensional manifold. By linearly mapping the PCA subspaces to one global low dimensional space, we obtain a ‘global’ low dimensional coordinate system for the data. As shown by Roweis et al., ensuring consistent global low-dimensional coordinates for the data can be expressed as a penalized likelihood optimization problem. We show that a restricted form of the Mixtures of Probabilistic PCA model allows for a more efficient algorithm. Experimental results are provided to illustrate the viability method.

Keywords

Density Model Informatics Institute Gaussian Density Feature Extraction Technique Probabilistic Principal Component Analyzer 
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 2002

Authors and Affiliations

  • Jakob J. Verbeek
    • 1
  • Nikos Vlassis
    • 1
  • Ben Kröse
    • 1
  1. 1.Informatics InstituteUniversity of AmsterdamAmsterdamThe Netherlands

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