[pymvpa] PCA transformation prior to SVM classification

[Jakob Scherer]

2010/11/29 Yaroslav Halchenko <debian at onerussian.com>:
>
> On Mon, 29 Nov 2010, Jakob Scherer wrote:
>> > actually it depends... e.g. if underlying classifier's regularization is
>> > invariant to the transformation (e.g. margin width), then yeap -- there should
>> > be no effect.  But if it is sensitive to it (e.g. feature selection , like in
>> > SMLR), then you might get advantage since, like in the case of SMLR, the goal
>> > of having fewer important features might be achieved with higher
>> > generalization.
>> A follow-up question; is the inverse true too: can having fewer
>> important features lead to a higher generalization?
>
> if you are asking:
>
> * "can having fewer important features among bulk of irrelevant features"
>  then I guess answer is "No"
>
> * "can having fewer features (just important ones)..."
>  then the answer "oh Yes" -- that is the goal of feature selection
>  procedures, to distill featureset so only important ones are left
>
> or did I misunderstand entirely?

Actually i wanted to ask: is it possible to get a higher performance
by feature selection?