This is with reference to projection pursuit regressions. I kind of get the idea behind approximating a continuous function using weighted sums of ridge functions. I am not sure why ridge functions work in the model. Or put another way, why work with a multivariate function that has values in one direction only? Why not just use a univariate function instead?
Also, the model works by generating 1 dimensional projections of multidimensional data. I understand the need for dimensionality reduction (curse of dimensionality) but why is looking for structure in the data's 1 dimensional projection equivalent to looking for structure in the data itself?
I hope I was able to frame the question right. Any help would be much appreciated
thanks a lot in advance !