A claimed stationary-point recovery bound for nonconvex low-rank matrix estimators is derived, with an application to errors-in-variables regression whose probabilistic dimension rates are not supported.
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Low-rank matrix recovery via nonconvex optimization methods with application to errors-in-variables matrix regression
A claimed stationary-point recovery bound for nonconvex low-rank matrix estimators is derived, with an application to errors-in-variables regression whose probabilistic dimension rates are not supported.