  Maximum Variance Unfolding is one of the main methods for (nonlinear)
dimensionality reduction. We study its large sample limit, providing specific
rates of convergence under standard assumptions. We find that it is consistent
when the underlying submanifold is isometric to a convex subset, and we provide
some simple examples where it fails to be consistent.
