Active Transfer Learning

A major assumption in data mining and machine learning is that the training set and test set come from the same domain. They share the same feature space and have the same distribution. However, in many real-world applications, the training set and test set usually come from different domains. Thus, there might be negative similarities between different domains so that the negative transfer problem caused by negative similarity may happen. In this paper, we propose a novel method named active transfer learning (ATL) to solve the above problem. Specifically, the orthogonal projection matrix and the weight coefficient vector are introduced to extend maximum mean discrepancy (MMD) so that it can minimize MMD and simultaneously eliminate the negative transfer. To find the informative and discriminative subsets from the source domain, we then propose an information diversity term by using the local geometric structure information of the source samples. Besides, by using the label information of source samples, our method can guarantee the selected subsets as discriminative as possible. Finally, to efficiently implement the proposed method, an alternating optimization approach, which is based on the alternating direction method of multipliers (ADMM), is designed to solve the optimization problem. To demonstrate the effectiveness of the proposed ATL model, experiments are conducted on five real-world data sets. The experimental results show the superiority of our method over the state-of-the-art methods.