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Robust Principal Component Analysis is a modification of the widely used statistical procedure of principal component analysis which works well with respect to grossly corrupted observations. A number of different approaches exist for Robust PCA, including an idealized version of Robust PCA, which aims to recover a low-rank matrix L0 from highly corrupted measurements M = L0 +S0. This decomposition in low-rank and sparse matrices can be achieved by techniques such as Principal Component Pursuit method , Stable PCP, Quantized PCP, Block based PCP, and Local PCP. Then, optimization methods are used such as the Augmented Lagrange Multiplier Method , Alternating Direction Method , Fast Alternating Minimization , Iteratively Reweighted Least Squares or alternating projections.