Fig. 2.

Illustration of the single kernel SVM classification procedure. For each image i, the signal in each voxel is extracted and concatenated in the feature vector x_m,i according to the M different regions defined by the AAL atlas. Each vector is associated to a label y_i (+1 or −1 in the case of binary classification). A linear kernel K_m is then built from the feature vectors for each region m (m = 1, …, M). The computed kernels K_m are added to obtain a whole brain linear kernel K. The kernel and its associated labels are used to train the model and estimate the model parameters w. The model can then be applied to new/unseen data x* to obtain an associated predicted label