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.
Domains
Machine Learning [cs.LG]
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