$\ell_{0}$-Norm Penalty Embedded Feature Selection in Universum Learning

This research paper describes a new machine learning method that combines Universum learning (using unlabeled data from the same domain as labeled training data to improve model training) with feature selection (choosing the most important input variables). The authors add an L0-norm penalty (a mathematical constraint that forces the model to use fewer features) to a support vector machine classifier, and develop an algorithm to solve this optimization problem efficiently.