I had heard that there is a body of literature devoted to the following problem:
You have a dataset and you produced a good predictive model for it. Now you have a different dataset, derived from different instruments, or different data sources, but similar enough that you can hope to scale one to the other so that you can use the predictive model on it.
Alternately, you do not have enough data to produce a model and hope that combining different datasets by scaling one to the other will help you reach the quantity of data you need.
First off what is this problem called so that I can search it more effectively? And also does anyone know of a good recent survey of techniques for this, either in book or paper form? I currently have access to most academic journals so those links work for me as well.
Say I have dataset A that I have a model for, dataset B occupies the same database schema but is from a difference source with different factors that are not included in my feature set.
Initially my intuition was to construct a QQplot and fit lines (or curves) to features that I thought should be similar. If the difference in the way feature 1 from A increases is similar to the way feature 1 from B increases but with a constant factor then fitting a line can reveal this factor. If the difference was exponential or logarithmic then I could scale using a fitted function. In this way I could constrain the way one variable increased to fit how another variable increased.
However this is just my intuition. I can certainly test it for overfitting but when I had heard that there was a lot of literature devoted to this subject then it seemed as though I should learn a few ways in which I could question my assumptions. It would probably be good for me to review the literature.
Does anyone know what the tag for this literature might be?