[pymvpa] mutliclass SVM weights (sensitivity measures)

[Vadim Axel]

Hi Michael, Yaroslav et al.,

1. I ran the site example (
http://www.pymvpa.org/measures.html#linear-svm-weights).  When I have three
classes to classifiy, there are two vectors of weights and each additional
class will add an additional vector of weights (I mean before averaging in
sens.py). If the classification is done one vs. rest or alternatively
pair-wise, shouldn't I get three vector of weights in my three class
classification? Actually, there was the same number of weight vectors in
matlab LIBSVM implementaion, so I guess I am missing something conceptual.

2.  I the site example I used my my dataset (7 voxels ROI, 3 classes). Four
of my voxels are noise and three other contain easily separable data.
Indeed, the mutliclass classification error rate was 0. When I make three
possible two classes classifications the wights totally make sense (close to
zero for noise voxels and high values for not-noise). But the weights from
mutliclass are not very different for noise and non-noise voxels, which
looks strange. Probably the answer to first question will clarify this issue
too.

3. And now something more theoretical: suppose I am making the
classification of 5 gradually changing colors. The absolute BOLD activation
level doens't change significantly between classes, but I succesfully
classify my colors beyond chance level. Can the weights of the SVM be used
as a measure which voxel was more informative between red & orange
discrimination and some other voxel for different pairs. Something like
taking the highest weight amongst all the weigths of this voxel (similar to
Kamitani & Tong 2005). Does it make sense?

Thanks a lot for your help!
Vadim
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