Coordinating Principal Component Analyzers

  title={Coordinating Principal Component Analyzers},
  author={Jakob J. Verbeek and Nikos A. Vlassis and Ben J. A. Kr{\"o}se},
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… CONTINUE READING
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