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The state-of-the-art metric-learning algorithms cannot perform well for domain adaptation settings, such as cross-domain face recognition, image annotation, etc., because labeled data in the source domain and unlabeled ones in the target domain are drawn from different, but related distributions. In this paper, we propose the domain adaptation metric learning (DAML), by introducing a data-dependent regularization to the conventional metric learning in the reproducing kernel Hilbert space (RKHS). This data-dependent regularization resolves the distribution difference by minimizing the empirical maximum mean discrepancy between source and target domain data in RKHS. Theoretically, by using the empirical Rademacher complexity, we prove risk bounds for the nearest neighbor classifier that uses the metric learned by DAML. Practically, learning the metric in RKHS does not scale up well. Fortunately, we can prove that learning DAML in RKHS is equivalent to learning DAML in the space spanned by principal components of the kernel principle component analysis (KPCA). Thus, we can apply KPCA to select most important principal components to significantly reduce the time cost of DAML. We perform extensive experiments over four well-known face recognition datasets and a large-scale Web image annotation dataset for the cross-domain face recognition and image annotation tasks under various settings, and the results demonstrate the effectiveness of DAML.