  Multi-source domain adaptation aims at leveraging the knowledge from multiple
tasks for predicting a related target domain. Hence, a crucial aspect is to
properly combine different sources based on their relations. In this paper, we
analyzed the problem for aggregating source domains with different label
distributions, where most recent source selection approaches fail. Our proposed
algorithm differs from previous approaches in two key ways: the model
aggregates multiple sources mainly through the similarity of semantic
conditional distribution rather than marginal distribution; the model proposes
a \emph{unified} framework to select relevant sources for three popular
scenarios, i.e., domain adaptation with limited label on target domain,
unsupervised domain adaptation and label partial unsupervised domain adaption.
We evaluate the proposed method through extensive experiments. The empirical
results significantly outperform the baselines.
