Efficient Parametric Projection Pursuit Density Estimation

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.