Submitted by OutOfCharm t3_zgvr0h in MachineLearning
I am wondering, is there any relationship between the dimension of the input vector and the ability of the output that it can represent? (e.g. can I say that a 10-dimensional feature vector has better representation ability against a 5-dimensional one, assuming data are sufficient to train a model.) Or if not, can you suggest any reference to the formal induction that illustrates that relationship?
Zealousideal_Golf252 t1_iziufir wrote
It may not directly answer your question, but you can search for universal approximation theory.