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Coordinating principal component analyzers

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 sys- tem for the data. As shown by Roweis et al., ensuring consistent global low-dimensional coordinates for the data can be expressed as a penal- ized 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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Submitted on : Wednesday, February 16, 2011 - 5:13:34 PM
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Jakob Verbeek, Nikos Vlassis, Ben Krose. Coordinating principal component analyzers. International Conference on Artificial Neural Networks (ICANN '02), Aug 2002, Madrid, Spain. ⟨10.1007/3-540-46084-5_148⟩. ⟨inria-00321498⟩



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