Finding Relevant Subspaces in Neural Network Learning

Contents

A common technique for searching for relevant subspaces is that of "principal component analysis." Our main result is a proof that under an interesting set of conditions, a variation on this approach will find at least one vector (nearly) in the space we are looking for. A recursive approach can then be used to get additional such vectors.