Robust principal component analysis

<p>Robust Principal Component Analysis (RPCA) is a modification of the widely used statistical procedure of <a href="/facts/Principal_component_analysis/pCbVTqdR">principal component analysis</a> (PCA) which works well with respect to <i>grossly</i> 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 (PCP), Stable PCP, Quantized PCP, Block based PCP, and Local PCP. Then, optimization methods are used such as the <a href="/facts/Augmented_Lagrangian_method/8zamSYrl">Augmented Lagrange Multiplier</a> Method (ALM), <a href="/facts/Alternating_direction_method_of_multipliers/8zamSYrl">Alternating Direction Method</a> (ADM), Fast Alternating Minimization (FAM), Iteratively Reweighted Least Squares (IRLS ) 
or alternating projections (AP).
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Robust principal component analysis open-in-new

Robust principal component analysis