  We show that there are no spurious local minima in the non-convex factorized
parametrization of low-rank matrix recovery from incoherent linear
measurements. With noisy measurements we show all local minima are very close
to a global optimum. Together with a curvature bound at saddle points, this
yields a polynomial time global convergence guarantee for stochastic gradient
descent {\em from random initialization}.
