# Feature selection for unknown parametric model

Suppose one has about 500 points of 50 dimensional data that one knows a priori is derived from a parametric model (perhaps with some outliers). Does using this knowledge help in feature selection? I am interested in both the continuous and discrete response case, if they differ.

Another related general question I have is whether using linear models and criterion like AIC are generally useful for feature selection, or whether they depend on strong model assumptions. I have heard that it can be wise to use "non-parametric" methods of feature selection. Are there any guidelines here?

• Just for future reference, [name](url) will make a link. – Andy Jones Dec 14 '14 at 17:21
• @Helmut: In addition to pls package, recommended by @Peter Flom, plsdepot R package can also be used for performing PLS regression. See examples here and here. – Aleksandr Blekh Dec 15 '14 at 1:23