Figure 1
Architecture of the mid-level fusion analysis employed here on two data sets X 1 and X 2 . The same n samples are divided in g groups. a) eCVA is applied on each data set to determine the Canonical Variates CV 1 and CV 2 allowing the best discrimination and the corresponding scores T 1 and T 2 . b) The scores are merged and analyzed using PCA. The global scores T and super loadings PTare obtained. Class prediction is obtained based on T.