[pymvpa] Question about classifiers

[Serin Atiani, Dr]

Hello, 

I am doing a first brush analysis on my data using pymvpa. When I use a SVM classifier, which I think  theoratically makes more sense to use with my data I get a strange cross validation confusion matrix with one row that has high numbers, and the rest is mostly zeros or ones. I have 17 different classes that I train the classifier on, and this is an example of the cross validation confusion matrix I get

[[ 2  0  0  0  0  0  0  0  1  0  1  0  1  0  0  0  0]
 [16 17 17 16 16 16 17 17 17 17 17 15 16 16 16 16 16]
 [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0]
 [ 0  0  0  2  0  0  1  0  0  0  0  0  0  0  0  0  0]
 [ 0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0]
 [ 0  2  0  1  0  1  0  0  0  1  0  1  0  1  1  0  0]
 [ 0  0  0  0  2  1  2  0  0  0  0  0  0  0  0  0  1]
 [ 0  0  1  0  0  0  0  1  0  0  0  0  0  0  0  0  0]
 [ 0  0  0  1  0  0  0  0  2  0  0  1  0  0  1  0  1]
 [ 0  1  1  0  1  0  0  1  0  2  0  0  0  0  0  0  0]
 [ 1  0  1  0  0  0  0  0  0  0  1  0  1  0  0  1  0]
 [ 0  0  0  0  0  0  0  0  0  0  1  2  0  0  0  2  0]
 [ 0  0  0  0  0  1  0  0  0  0  0  0  1  0  0  0  0]
 [ 0  0  0  0  0  1  0  0  0  0  0  1  1  3  0  0  0]
 [ 0  0  0  0  0  0  0  1  0  0  0  0  0  0  1  0  1]
 [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0]
 [ 1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1]] 

Reducing the number of features, makes things a bit better but I still get one row that has large numbers. I tried also to group my classes and train the SVM classifier on the two most distinguishable ones, Nearest neighbour gives a 80% accuracy, with SVM it is slightly above chance with a confusion matrix that looks like this again.  
 
[[5 0   ]
 [15 20]

It doesn't look right, anybody has any thoughts about this?

Serin