  Over the last years, dictionary learning method has been extensively applied
to deal with various computer vision recognition applications, and produced
state-of-the-art results. However, when the data instances of a target domain
have a different distribution than that of a source domain, the dictionary
learning method may fail to perform well. In this paper, we address the
cross-domain visual recognition problem and propose a simple but effective
unsupervised domain adaption approach, where labeled data are only from source
domain. In order to bring the original data in source and target domain into
the same distribution, the proposed method forcing nearest coupled data between
source and target domain to have identical sparse representations while jointly
learning dictionaries for each domain, where the learned dictionaries can
reconstruct original data in source and target domain respectively. So that
sparse representations of original data can be used to perform visual
recognition tasks. We demonstrate the effectiveness of our approach on standard
datasets. Our method performs on par or better than competitive
state-of-the-art methods.
