  The reconstruction of low-rank matrix from its noisy observation finds its
usage in many applications. It can be reformulated into a constrained nuclear
norm minimization problem, where the bound $\eta$ of the constraint is
explicitly given or can be estimated by the probability distribution of the
noise. When the Lagrangian method is applied to find the minimizer, the
solution can be obtained by the singular value thresholding operator where the
thresholding parameter $\lambda$ is related to the Lagrangian multiplier. In
this paper, we first show that the Frobenius norm of the discrepancy between
the minimizer and the observed matrix is a strictly increasing function of
$\lambda$. From that we derive a closed-form solution for $\lambda$ in terms of
$\eta$. The result can be used to solve the constrained nuclear-norm-type
minimization problem when $\eta$ is given. For the unconstrained
nuclear-norm-type regularized problems, our result allows us to automatically
choose a suitable regularization parameter by using the discrepancy principle.
The regularization parameters obtained are comparable to (and sometimes better
than) those obtained by Stein's unbiased risk estimator (SURE) approach while
the cost of solving the minimization problem can be reduced by 11--18 times.
Numerical experiments with both synthetic data and real MRI data are performed
to validate the proposed approach.
