A Unified Framework for Domain Adaptation using Metric Learning on Manifolds

We present a novel framework for domain adaptation, whereby both geometric and statistical differences between a labeled source domain and unlabeled target domain can be reconciled using a unified mathematical framework that exploits the curved Riemannian geometry of statistical manifolds. We exploit a simple but important observation tha...More