Learning Using Privileged Information with L-1 Support Vector Machine

In the process of human learning, teachers always play an important role. However, for most of the existing machine learning method, the role of teachers is seldom considered. Recently, Vapnik introduced an advanced learning paradigm called Learning Using Privileged Information(LUPI) to include the elements of human teaching in machine learning. Through theoretical analysis and numerical experiments, the superiority of LUPI over the classical learning paradigm has received preliminary proof. In this paper, on the basis of existing work for LUPI, we introduce the privileged information into the modeling of L-1 support vector machine(SVM). Compared with the existing research of LUPI with L-2 SVM, the new method has the advantage of spending less time on tuning model parameters and the additional benefits of performing feature selection in the training process. Experiments on the digit recognition problem validate the effectiveness of our method.