Efficient algorithms for robust and stable principal component pursuit problems

The problem of recovering a low-rank matrix from a set of observations corrupted with gross sparse error is known as the robust principal component analysis (RPCA) and has many applications in computer vision, image processing and web data ranking. It has been shown that under certain conditions, th...

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Vydané v:	Computational optimization and applications Ročník 58; číslo 1; s. 1 - 29
Hlavní autori:	Aybat, Necdet Serhat, Goldfarb, Donald, Ma, Shiqian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Boston Springer US 01.05.2014 Springer Nature B.V
Predmet:	Algorithms Analysis Convex analysis Convex and Discrete Geometry Data smoothing Extraction Image processing Industrial engineering Management Science Mathematical analysis Mathematical models Mathematics Mathematics and Statistics Methods Noise reduction Operations Research Operations Research/Decision Theory Optimization Principal component analysis Principal components analysis Sparsity Statistics Studies Surveillance Iteration complexity Convex optimization Smoothing Alternating direction augmented Lagrangian method Alternating linearization method Accelerated proximal gradient method Compressed sensing Principal component analysis Matrix completion
ISSN:	0926-6003, 1573-2894
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