  In this paper, we propose a novel learning framework for the problem of
domain transfer learning. We map the data of two domains to one single common
space, and learn a classifier in this common space. Then we adapt the common
classifier to the two domains by adding two adaptive functions to it
respectively. In the common space, the target domain data points are weighted
and matched to the target domain in term of distributions. The weighting terms
of source domain data points and the target domain classification responses are
also regularized by the local reconstruction coefficients. The novel transfer
learning framework is evaluated over some benchmark cross-domain data sets, and
it outperforms the existing state-of-the-art transfer learning methods.
