  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.
