Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In statistics, projection pursuit regression (PPR) is a statistical model developed by Jerome H. Friedman and Werner Stuetzle which is an extension of additive models. This model adapts the additive models in that it first projects the data matrix of explanatory variables in the optimal direction before applying smoothing functions to these explanatory variables. The model consists of linear combinations of non-linear transformations of linear combinations of explanatory variables. Both projection pursuit regression and neural networks models project the input vector onto a one-dimensional hyperplane and then apply a nonlinear transformation of the input variables that are then added in a linear fashion. Thus both follow the same steps to overcome the curse of dimensionality. The main difference is that the functions fj being fitted in PPR can be different for each combination of input variables and are estimated one at a time and then updated with the weights, whereas is NN these are all specified upfront and estimated simultaneously.

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