[pymvpa] [mvpa-toolbox] Re: Normalised Data

[Yaroslav Halchenko]

On Wed, 16 Sep 2009, Sam Evans wrote:
>    The reason I asked about normalisation is that I recently conducted my
>    "first" pattern analysis and wanted to use an anatomical ROI to select
>    features. For expediency sake I normalised my data and used an off the
>    shelf ROI that had been defined in MNI space - as I wanted to ensure a
>    decent fit for the ROI.  I got a high performance level training on one
>    run and testing on the other and vice versa using a SVM.
congratulations! ;)  I wonder how 'high' the performance is actually? ;)

>    I wanted to know whether I might have unwittingly enhanced performance
>    in some way by normalising?
I think that critical step toward improvement of the results was not
normalization per se, but ROI selection.  Now you have less features,
SVM feels happier (feature selection some times is crucial step). It is
easy to verify if I am correct -- just take your ROI, invert your
'normalization' step to obtain ROI in the subject space and perform the
same analysis in that native space, where data wasn't perturbed by
normalization... would the results be comparable?

Since (if I understood you correctly) you chose ROI according to a
priori assumptions (not per se due to some previous analysis of the same
data using other 'localization' methods) -- I think nothing in your
actions could be classified as 'double dipping', hence you are safe from
this perspective ;)

>  I'd be really interested in what you
>    think.  Later I intend to do the analysis in native space using
>    individually defined ROIs but this was my first pass at the data - and
>    the results seems promising - to me at least?
d'oh -- I've started replying too early I guess... yes -- so far, so
good.  There is plenty of other 'steps' where evil things could happen
though... ;-)

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