rpca: RobustPCA: Decompose a Matrix into Low-Rank and Sparse Components

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Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Candes, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust principal component analysis?. Journal of the ACM (JACM), 58(3), 11. prove that we can recover each component individually under some suitable assumptions. It is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This package implements this decomposition algorithm resulting with Robust PCA approach.

Author
Maciek Sykulski [aut, cre]
Date of publication
2015-07-31 01:15:38
Maintainer
Maciek Sykulski <macieksk@gmail.com>
License
GPL-2 | GPL-3
Version
0.2.3

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Man pages

F2norm
Frobenius norm of a matrix
rpca
Decompose a matrix into a low-rank component and a sparse...
rpca-package
\Sexpr[results=rd,stage=build]{tools:::Rd_package_title("rpca")}
thresh.l1
Shrinkage operator
thresh.nuclear
Thresholding operator

Files in this package

rpca
rpca/NAMESPACE
rpca/R
rpca/R/robustpca.R
rpca/MD5
rpca/build
rpca/build/partial.rdb
rpca/DESCRIPTION
rpca/man
rpca/man/rpca-package.Rd
rpca/man/rpca.Rd
rpca/man/F2norm.Rd
rpca/man/thresh.l1.Rd
rpca/man/thresh.nuclear.Rd