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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Communication dans un congrès
José R. Dorronsoro. International Conference on Artificial Neural Networks (ICANN '02), Aug 2002, Madrid, Spain. Springer, 2415, 2002, Lecture Notes in Computer Science (LNCS). 〈http://www.springerlink.com/content/rdgmchfwhgefpu00/〉. 〈10.1007/3-540-46084-5_148〉
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Jakob Verbeek, Nikos Vlassis, Ben Krose. Coordinating principal component analyzers. José R. Dorronsoro. International Conference on Artificial Neural Networks (ICANN '02), Aug 2002, Madrid, Spain. Springer, 2415, 2002, Lecture Notes in Computer Science (LNCS). 〈http://www.springerlink.com/content/rdgmchfwhgefpu00/〉. 〈10.1007/3-540-46084-5_148〉. 〈inria-00321498〉

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