Feature attributes are extracted from an observation space to create feature
vectors for each class to be identified. A linear transformation matrix is used
to reduce the dimension of the feature vectors. A numerical optimization algorithm
maximizes a geometric criterion function in order to calculate the linear transformation
matrix, where it exploits the geometry of the class contours of constant density.
Next, a classifier based on the feature vectors in a lower dimension is generated
and a class is determined for the data represented.