Domain adaptation via support vector machine based on scatter difference

Domain adaptation methods show better ability to learn when the training data is not identically and independently distributed. The key task of domain adaptation is to find a suitable measure to scale the distributed difference between source domain and target domain. So a projected maximum divergence discrepancy distance measure is proposed. Based on the structural risk minimization theory and the projected maximum divergence discrepancy distance measure, the support vector machine based on difference of divergence is also proposed. Experimental results on artificial and real world problems show the proposed approach could learn across the domains effectively.