  Although achieving remarkable progress, it is very difficult to induce a
supervised classifier without any labeled data. Unsupervised domain adaptation
is able to overcome this challenge by transferring knowledge from a labeled
source domain to an unlabeled target domain. Transferability and
discriminability are two key criteria for characterizing the superiority of
feature representations to enable successful domain adaptation. In this paper,
a novel method called \textit{learning TransFerable and Discriminative Features
for unsupervised domain adaptation} (TFDF) is proposed to optimize these two
objectives simultaneously. On the one hand, distribution alignment is performed
to reduce domain discrepancy and learn more transferable representations.
Instead of adopting \textit{Maximum Mean Discrepancy} (MMD) which only captures
the first-order statistical information to measure distribution discrepancy, we
adopt a recently proposed statistic called \textit{Maximum Mean and Covariance
Discrepancy} (MMCD), which can not only capture the first-order statistical
information but also capture the second-order statistical information in the
reproducing kernel Hilbert space (RKHS). On the other hand, we propose to
explore both local discriminative information via manifold regularization and
global discriminative information via minimizing the proposed \textit{class
confusion} objective to learn more discriminative features, respectively. We
integrate these two objectives into the \textit{Structural Risk Minimization}
(RSM) framework and learn a domain-invariant classifier. Comprehensive
experiments are conducted on five real-world datasets and the results verify
the effectiveness of the proposed method.
