On a regularization of unsupervised domain adaptation in RKHS

We analyze the use of the so-called general regularization scheme in the scenario of unsupervised domain adaptation under the covariate shift assumption. Learning algorithms arising from the above scheme are generalizations of importance weighted regularized least squares method, which up to now is among the most used approaches in the covariate shift setting. We explore a link between the considered domain adaptation scenario and estimation of Radon-Nikodym derivatives in reproducing kernel Hilbert spaces, where the general regularization ∗Elke.Gizewski@i-med.ac.at †lukas.mayer@i-med.ac.at ‡Bernhard.moser@scch.at §duc.nguyen@ricam.oeaw.ac.at ¶sergiy.pereverzyev@i-med.ac.at ‖sergei.pereverzyev@oeaw.ac.at ∗∗Natalia.shepeleva@scch.at ††Werner.Zellinger@scch.at