# How to find "relationships" between two data representations?

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.

• Have you tried a simple design where you input one representation to an NN and make it predict the other? Sep 18, 2021 at 18:59
• You say "There is neither apparent one-to-one correspondence nor both need to be the same lengths", but then I don't understand why you want to convert one representation to the other. What's your ultimate goal or why do you want to do this? Could you also provide an example of 2 vastly different feature vectors that you would want the algorithm to convert from/to, so that we have a better idea of what you really want to achieve?
– nbro
Sep 19, 2021 at 0:43
• Yes, I am sorry that the message was kind of cryptic. I am comparing chemical vibrations to musical notes. I have a dataset of chemicals, let's say proteins, polymers, crystals etc. and another dataset of musical notes, ragas etc. I need to find the correspondence or any hidden structural similarities between these two domains. I can assign one amino acid to one note manually and create some kind of music but that's not what I want. In the end, the algorithm needs to convert chemicals to music or music to chemicals vice versa. Sep 30, 2021 at 22:01

Well, I suppose one can use some kind of contrastive learning in this case.