  Domain adaptation aims to leverage information from the source domain to
improve the classification performance in the target domain. It mainly utilizes
two schemes: sample reweighting and feature matching. While the first scheme
allocates different weights to individual samples, the second scheme matches
the feature of two domains using global structural statistics. The two schemes
are complementary with each other, which are expected to jointly work for
robust domain adaptation. Several methods combine the two schemes, but the
underlying relationship of samples is insufficiently analyzed due to the
neglect of the hierarchy of samples and the geometric properties between
samples. To better combine the advantages of the two schemes, we propose a
Grassmannian graph-attentional landmark selection (GGLS) framework for domain
adaptation. GGLS presents a landmark selection scheme using attention-induced
neighbors of the graphical structure of samples and performs distribution
adaptation and knowledge adaptation over Grassmann manifold. the former treats
the landmarks of each sample differently, and the latter avoids feature
distortion and achieves better geometric properties. Experimental results on
different real-world cross-domain visual recognition tasks demonstrate that
GGLS provides better classification accuracies compared with state-of-the-art
domain adaptation methods.
