  In this work we show that the classification performance of high-dimensional
structural MRI data with only a small set of training examples is improved by
the usage of dimension reduction methods. We assessed two different dimension
reduction variants: feature selection by ANOVA F-test and feature
transformation by PCA. On the reduced datasets, we applied common learning
algorithms using 5-fold cross-validation. Training, tuning of the
hyperparameters, as well as the performance evaluation of the classifiers was
conducted using two different performance measures: Accuracy, and Receiver
Operating Characteristic curve (AUC). Our hypothesis is supported by
experimental results.
