  Domain adaptation aims to transfer knowledge of labeled instances obtained
from a source domain to a target domain to fill the gap between the domains.
Most domain adaptation methods assume that the source and target domains have
the same dimensionality. Methods that are applicable when the number of
features is different in each domain have rarely been studied, especially when
no label information is given for the test data obtained from the target
domain. In this paper, it is assumed that common features exist in both domains
and that extra (new additional) features are observed in the target domain;
hence, the dimensionality of the target domain is higher than that of the
source domain. To leverage the homogeneity of the common features, the
adaptation between these source and target domains is formulated as an optimal
transport (OT) problem. In addition, a learning bound in the target domain for
the proposed OT-based method is derived. The proposed algorithm is validated
using both simulated and real-world data.
