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The nuclear norm is an effective proxy for matrix rank in a range of minimization problems, including subspace identification. Nuclear norm-based methods are implemented via iterative optimization methods and in problems with very noisy data the quality of the nuclear norm-based estimate may warrant the additional computation cost. We present two methods (based on the dual accelerated gradient projection...
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