Fig. 1.

Classification. The top row in panel A shows how a classification model can be trained on neuroimaging data. First feature extraction methods are used to identify features that can be used to train a classification model on samples with known labels. Once the classification model is trained, it can be applied to features extracted (using the same procedure) from subjects with unknown labels as indicated in the bottom row. *In principle the feature extraction step can be omitted. However, in practice for many imaging modalities (including fMRI), overfitting due to the high dimensionality of the input data will be detrimental to the classification performance. Panel B provides an illustration of the linear soft margin SVC algorithm in a 2-dimensional feature space. The SVC identifies the separating hyperplane that maximizes the margin, this hyperplane is only defined by the support vectors which are samples that are on the margin (marked by a circle). The soft margin SVC allows misclassification to avoid overfitting by introducing slack variables for each misclassified sample (marked with a dotted line). When the SVC is trained the labels of new samples (marked in gray) can be estimated according to the side of the hyperplane on which they reside.