  We propose a novel supervised multi-class/single-label classifier that maps
training data onto a linearly separable latent space with a simplex-like
geometry. This approach allows us to transform the classification problem into
a well-defined regression problem. For its solution we can choose suitable
distance metrics in feature space and regression models predicting latent space
coordinates. A benchmark on various artificial and real-world data sets is used
to demonstrate the calibration qualities and prediction performance of our
classifier.
