Extending the learning using privileged information paradigm to logistic regression

Abstract

Learning using privileged information paradigm is a learning scenario that exploits privileged features, available at training time, but not at prediction, as additional information for training models. This paper delves into the learning of logistic regression models using privileged information. We provide two new algorithms. For its development, the parameters of a conventional logistic regression trained with all available features, privileged and regular, are projected onto the parameter space associated to regular features (available at training and prediction time). The projection to obtain the model parameters is performed by the minimization of two different loss functions governed by logit terms and posterior probabilities. In addition, a metric is proposed to determine whether the use of privileged information can enhance performance. Experimental results report improvements of our proposals over the performance of conventional logistic regression learned without privileged information.