Though some people say mathematics is frustratingly difficult, all must agree that one part of it is frustratingly easy. Or at least agree that the title of a particular study is “Frustratingly Easy Semi-supervised Domain Adaptation.” Written by Abhishek Kumar, Avishek Saha, Hal Daume III, P. Thomas Fletcher and Suresh Venkatasubramanian, its abstract clarifies matters, more or less:
In this work, we propose a semi-supervised extension to a well-known supervised domain adaptation approach (EA). Our proposed approach (EA++) builds on the notion of augmented space (introduced in EA) and harnesses unlabeled data in target domain to ameliorate the transfer of information from source to target. This semi-supervised approach to domain adaptation is extremely simple to implement, and can be applied as a pre-processing step to any supervised learner.
(Thanks to investigator Stanley Eigen for bringing it to our attention.)