Thanks. The implication seems to be that if you restrict the # of parameters so it's equal to intrinsic dimension, learning isn't possible, but it is possible with this random projections method. Wonder why. It seems like with both methods, the number of possibilities being explored is the same, but the higher-parameter model space is richer with solutions for some reason.