Homogeneous Online Transfer Learning with Online Distribution Discrepancy Minimization
This work addresses the challenge of adapting transfer learning to online settings, which is incremental but important for applications where target data arrives sequentially.
The paper tackles the problem of online transfer learning by proposing a method that reduces distribution discrepancy between source and target domains in an online manner, achieving significant performance improvements over state-of-the-art methods on real-world datasets.
Transfer learning has been demonstrated to be successful and essential in diverse applications, which transfers knowledge from related but different source domains to the target domain. Online transfer learning(OTL) is a more challenging problem where the target data arrive in an online manner. Most OTL methods combine source classifier and target classifier directly by assigning a weight to each classifier, and adjust the weights constantly. However, these methods pay little attention to reducing the distribution discrepancy between domains. In this paper, we propose a novel online transfer learning method which seeks to find a new feature representation, so that the marginal distribution and conditional distribution discrepancy can be online reduced simultaneously. We focus on online transfer learning with multiple source domains and use the Hedge strategy to leverage knowledge from source domains. We analyze the theoretical properties of the proposed algorithm and provide an upper mistake bound. Comprehensive experiments on two real-world datasets show that our method outperforms state-of-the-art methods by a large margin.