[pymvpa] Spatiotemporal Feature Selection

[Roberto Guidotti]

Dear all,

I've another question about spatiotemporal analysis. In my dataset I have 6
runs per task with approximately a hundred volumes each task each run and
running a classification on only 6 examples is very overfitting-oriented,
how can I do to reduce the overfitting? I've tried with a leave 3 run out
crossvalidation in order to have more testing runs and a very general model
fitted only on 3 runs (I use SVM)? Are there some other strategies?

Thank you,
Roberto

On 19 July 2012 13:01, Roberto Guidotti <robbenson18 at gmail.com> wrote:

> Dear all,
>
> I'm working on spatiotemporal analysis of fMRI data, in this case the
> number of features increases drammatically and I want to perform a Feature
> Selection.
> In the toolbox the Feature Selection of event related datasets is done
> watching the spatiotemporal features variation across condition or checking
> the single feature. I think it could be useful to select voxels that
> temporally varies across experiment.
>
> It is possible to perform a sort of spatiotemporal feature selection, if
> not yet implemented? Or I'm asking a question theoretically wrong?
>
> Thank you
> Roberto.
>
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