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This paper studies tensor-based Robust Principal Component Analysis (RPCA) using atomic-norm regularization.
Some mathematical notes on three-mode factor analysis
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Level-set methods for convex optimization
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Noisy tensor completion via the sum-of-squares hierarchy
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Nuclear norm of higher-order tensors
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