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 land-