The performance of a machine learning model degrades when it is applied to
data from a similar but different domain than the data it has initially been
trained on. To mitigate this domain shift problem, domain adaptation (DA)
techniques search for an optimal transformation that converts the (current)
input data from a source domain to a target domain to learn a domain-invariant
representation that reduces domain discrepancy. This paper proposes a novel
supervised DA based on two steps. First, we search for an optimal
class-dependent transformation from the source to the target domain from a few
samples. We consider optimal transport methods such as the earth mover's
distance, Sinkhorn transport and correlation alignment. Second, we use
embedding similarity techniques to select the corresponding transformation at
inference. We use correlation metrics and higher-order moment matching
techniques. We conduct an extensive evaluation on time-series datasets with
domain shift including simulated and various online handwriting datasets to
demonstrate the performance