If we ignore the domain difference, and treat the labeled source domain instances as labeled data and the unlabeled target domain instances as unlabeled data, then we are facing a semi-supervised learning (SSL) problem. We can then apply any SSL algorithms (Chapelle et al., 2006; Zhu, 2005) to the domain adaptation problem. The subtle difference between SSL and domain adaptation is that (1) the amount of labeled data in SSL is small but large in domain adaptation, and (2) the labeled data may be noisy in domain adaptation if we do not assume for all , whereas in SSL the labeled data is all reliable.
There has been some work extending semi-supervised learning methods for domain adaptation. Dai et al. (2007a) proposed an EM-based algorithm for domain adaptation, which can be shown to be equivalent to a semi-supervised EM algorithm (Nigam et al., 2000) except that Dai et al. proposed to estimate the trade-off parameter between the labeled and the unlabeled data using the KL-divergence between the two domains. Jiang and Zhai (2007a) proposed to not only include weighted source domain instances but also weighted unlabeled target domain instances in training, which essentially combines instance weighting with bootstrapping. Xing et al. (2007) proposed a bridged refinement method for domain adaptation using label propagation on a nearest neighbor graph, which has resemblance to graph-based semi-supervised learning algorithms (Chapelle et al., 2006; Zhu, 2005).