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. © Springer-Verlag Berlin Heidelberg 2002.
CITATION STYLE
Verbeek, J. J., Vlassis, N., & Kröse, B. (2002). Coordinating principal component analyzers. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 914–919). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_148
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