I am a researcher in a field, and new to the whole of AI and machine learning techniques. May the following question is trivial or not framed in the ML language but I try my best.
I have two sets of representations (I can extract feature vectors, etc., from the datasets) from vastly different domains. I want to find, if any, a relationship exists between these two sets. In other words, I want an algorithm (the idea of an algorithm) to learn both representations and find the connections and convert one representation to another.
There is neither apparent one-to-one correspondence nor both need to be the same lengths.
Any suggestion on how to approach this problem is appreciated.
I thought of one method; write an encoder-decoder for each of these presentations separately and swap the decoders. I am not sure whether it works or not, and besides I may not have any idea what's going on there.
I prefer a general approach if it exists.