I have two separate and heterogeneous measurements of the same object. I wish to make predictions about the object state using both sets of measurements.
What ways can the measurements be combined within a regression framework in order to improve inference?
What I mean by "heterogeneous" is that the measurements are disparate and incommensurate. Two different devices were used to observe the object, which measured different aspects of its state.
In relation to improved inference, I wish to predict the object state using the two measurements using regression. Ideally, using both measurements should lead to a more accurate prediction than using either of the measurements separately.
How should the state predictions be based on the measurements? At the moment I am learning a mapping from the measurement input space to the output space using ridge regression.