I understand supervised and unsupervised learning well, and would be able to identify some 'basic' examples of, for example, supervised classifcation as:
- Random Forests
- Logistic Regression
These are key works in the field which have lots of code and publications available.
I am now starting to look at domain adaptation in supervised learning, where the distribution over the data at learning and testing time are known to be different. Despite reading some of the literature, I haven't spotted any similar 'basic' methods which come up time and time again. In contrast, there seem to be a wild array of completely different methods for achieving the goal, many of which have only been in the literature for a few years.
Are there such key, established methods for domain adaptation? What are the most popular methods currently used?