[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.
Disciplines :
Computer science
Identifiers :
UNILU:UL-ARTICLE-2011-738
Author, co-author :
Verbeek, Jakob J.
Vlassis, Nikos ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Kröse, Ben J. A.
Language :
English
Title :
Coordinating Principal Component Analyzers
Publication date :
2002
Event name :
Proc. Int. Conf. on Artificial Neural Networks, Madrid, Spain,
Event date :
2002
Main work title :
Proc. Int. Conf. on Artificial Neural Networks, Madrid, Spain,
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