This paper is a contribution to solving the domain adaptation problem where no labeled target data is available. A new SVM approach is proposed by imposing a zero-valued Maximum Mean Discrepancy-like constraint. This heuristic allows us to expect a good similarity between source and target data, after projection onto an efficient subspace of a Reproducing Kernel Hilbert Space. Accordingly, the classifier will perform well on source and target data. We show that this constraint does not modify the quadratic nature of the optimization problem encountered in classic SVM, so standard quadratic optimization tools can be used. Experimental results demonstrate the competitiveness and efficiency of our method.
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