Figure 4.

The figure shows features for controls versus patients after each feature extraction step. Each dot represents an individual and the color of the dot indicates the correct diagnosis of either control (blue) or schizophrenia (red). Individuals are close to each other if the Euclidean distances between training data are small. The original training samples cannot be separated by a linear classifier. A two-level feature identification step is used to select significantly different voxels. After KPCA, most of training samples separate to two groups. Training samples are linearly separable and a maximum margin is obtained after FLD. Parameters in two-level feature identification step are t₁ = 0.5 and t₂ = 0.5, which are selected during the training stage using the DMN component (from rest data) as input to the framework.