On the convergence of maximum variance unfolding

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

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