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This paper aims to recover a low-rank matrix and a sparse matrix from their superposition observed in additive white Gaussian noise by formulating a convex optimization problem with a non-separable non-convex regularization. The proposed nonconvex penalty function extends the recent work of a multivariate generalized minimax-concave penalty for promoting sparsity. It avoids underestimation charact...

Stable Principal Component Pursuit via Convex Analysis