I have an input-output system, which is fully determined by 256 parameters, of which I know a significant amount are of less importance to the input-output pattern.

The data I have is some (64k in total) input-parameter-output match.

My goal is to compress these 256 parameters to a smaller scale (like 32) using an encoder of some kind while being able to preserve the response pattern.

But I can't seem to find a proper network for this particular problem, because I'm not trying to fit these parameters (they all have a mean of one and variance of 1/4), but rather its influence on the output, so traditional data-specific operations will not work in this case.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.