Efficient Parametric Projection Pursuit Density Estimation
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.
Cite
Text
Welling et al. "Efficient Parametric Projection Pursuit Density Estimation." Conference on Uncertainty in Artificial Intelligence, 2003.Markdown
[Welling et al. "Efficient Parametric Projection Pursuit Density Estimation." Conference on Uncertainty in Artificial Intelligence, 2003.](https://mlanthology.org/uai/2003/welling2003uai-efficient/)BibTeX
@inproceedings{welling2003uai-efficient,
title = {{Efficient Parametric Projection Pursuit Density Estimation}},
author = {Welling, Max and Zemel, Richard S. and Hinton, Geoffrey E.},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2003},
pages = {575-582},
url = {https://mlanthology.org/uai/2003/welling2003uai-efficient/}
}