Random Matrix Theory

Between Rosenow, et al and Potter, et al I've figured out a few ways people suggest to 'clean' a 'noisy' correlation matrix. Basically take the largest eigenvalues and either average the rest, or set the rest to 0 depending, reconstitute the matrix and set the diagonal to 1. By setting the noisy eigenvalues to 0, you have a sort of shrinkage towards uncorrelated assets which is interesting. I haven't quite thought through what the implication of that is on a mean-variance optimization.