Efficient parametric projection pursuit density estimation

Authors:
Max Welling;Richard S. Zemel;Geoffrey E. Hinton
Affiliations:
Dept. of Computer Science, University of Toronto, Toronto;Dept. of Computer Science, University of Toronto, Toronto;Dept. of Computer Science, University of Toronto, Toronto
Venue:
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Year:
2002

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Abstract

Product models of low dimensional experts are a powerful way to avoid the curse of dimensionality. We present the "undercomplete product of experts" (UPoE), where each expert models a one dimensional projection of the data. The UPoE may be interpreted as a parametric probabilistic model for projection pursuit. Its ML learning rules are identical to the approximate learning rules proposed before for under-complete ICA. We also derive an efficient sequential learning algorithm and discuss its relationship to projection pursuit density estimation and feature induction algorithms for additive random field models.