Fig. 3.

Illustration of the multiple kernel learning 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 kernels and their associated labels are used to train the model. First, model parameters w_m are estimated to define a decision function f_m per kernel. The weight of each decision function, d_m, is then estimated to provide a final decision function f(x). The model can then be applied to new/unseen data x* to obtain an associated predicted label, based on feature vectors defined using the same atlas, x₁*, x₂*, …, x_M*