  In this paper, we propose a novel domain adaptation method that can be
applied without target data. We consider the situation where domain shift is
caused by a prior change of a specific factor and assume that we know how the
prior changes between source and target domains. We call this factor an
attribute, and reformulate the domain adaptation problem to utilize the
attribute prior instead of target data. In our method, the source data are
reweighted with the sample-wise weight estimated by the attribute prior and the
data themselves so that they are useful in the target domain. We theoretically
reveal that our method provides more precise estimation of sample-wise
transferability than a straightforward attribute-based reweighting approach.
Experimental results with both toy datasets and benchmark datasets show that
our method can perform well, though it does not use any target data.
