Figure 3.

Feature extraction scheme. The original data with two classes (red squares vs blue circles) and multiple variables [left-hand side; as an example, two variables (V2 and V5) are plotted against each other resulting in no separation/pattern, and in fact no other selection would show a clear separation] are transformed using an unsupervised method (here PCA) to a new feature space (right-hand side; Scores plot) such that the first two latent variables encompass most relevant information (variance) that clearly separates two groups in a far reduced number of variables (indicating separation/pattern). In this example plotting of original data and finding which variables plotted against each other provides any pattern would be very difficult whereas with PCA requires only first two variables to answer the question if there is any pattern/separation.