Reference : Coordinating Principal Component Analyzers
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Coordinating Principal Component Analyzers
Verbeek, Jakob J. [> >]
Vlassis, Nikos mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Kröse, Ben J. A. [> >]
Proc. Int. Conf. on Artificial Neural Networks, Madrid, Spain,
Proc. Int. Conf. on Artificial Neural Networks, Madrid, Spain,
[en] 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.

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