In this thesis we present a new semi-supervised classification technique based on the Kernel Entropy Component Analysis (KECA) transformation and the least absolute shrinkage selection operator (LASSO). The latter is a constrained version of the least squares classifier. Traditional supervised classification techniques only use a limited set of labeled data to train the classifier, thus leaving ...