I've been reading a lot recently about the concept of joint regularization in computer vision. Joint regularization builds on the observation that when learning multiple related concepts, for example "cat" and "dog" the most of the useful features to classify something as "cat" should be useful to classify something as "dog".
So a problem specific regularization term is designed. In the case I previously explained, the regularization term encourages "sharing" of useful useful features by mixing $L_1$ and $L_inf$ regularization terms. This is called joint regularization.
So, my questions are:
- Are there other types of problems (possibly outside of computer vision) where some type of "problem specific" regularization is used and is successful?
- Are there other mixes of regularization terms that have been successfully applied?