  In this paper, we address the problem of unsupervised Domain Adaptation. The
need for such an adaptation arises when the distribution of the target data
differs from that which is used to develop the model and the ground truth
information of the target data is unknown. We propose an algorithm that uses
optimal transport theory with a verifiably efficient and implementable solution
to learn the best latent feature representation. This is achieved by minimizing
the cost of transporting the samples from the target domain to the distribution
of the source domain.
