Uncertainty in Artificial Intelligence
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Efficient Parametric Projection Pursuit Density Estimation
Max Welling, Richard Zemel, Geoffrey Hinton
Abstract:
Product models of low dimensional experts are a powerful way to avoid the curse of dimensionality. We present the ``under-complete product of experts' (UPoE), where each expert models a one dimensional projection of the data. The UPoE is fully tractable and 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
Keywords:
Pages: 575-582
PS Link:
PDF Link: /papers/03/p575-welling.pdf
BibTex:
@INPROCEEDINGS{Welling03,
AUTHOR = "Max Welling and Richard Zemel and Geoffrey Hinton",
TITLE = "Efficient Parametric Projection Pursuit Density Estimation",
BOOKTITLE = "Proceedings of the Nineteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-03)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2003",
PAGES = "575--582"
}


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