I have "millions" of items each having N binary features. When a feature is "0" it could be that the information is simply missing. So, given the data with the currently observed 1's, I would like to have a probability of the "0" features being "1".
I am thinking this can be a Neural network with all features as input and same as output. But then I don't know how the training would work. I don't have ground truth.
I would like some help expressing my problem and hopefully not reinvent the wheel. Is this is a classical problem in ML, and what approach can be applied?