Transfer learning with one-class data

When training and testing data are drawn from different distributions, most statistical models need to be retrained using the newly collected data. Transfer learning is a family of algorithms that improves the classifier learning in a target domain of interest by transferring the knowledge from one or multiple source domains, where the data falls in a different distribution. In this paper, we consider a new scenario of transfer learning for two-class classification, where only data samples from one of the two classes (e.g., the negative class) are available in the target domain. We introduce a regression-based one-class transfer learning algorithm to tackle this new problem. In contrast to the traditional discriminative feature selection, which seeks the best classification performance in the training data, we propose a new framework to learn the most transferable discriminative features suitable for our transfer learning. The experiment demonstrates improved performance in the applications of facial expression recognition and facial landmark detection. (C) 2013 Published by Elsevier B.V.