[pymvpa] PCA transformation prior to SVM classification
jakob.scherer at gmail.com
Mon Nov 29 18:08:36 UTC 2010
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?
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