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

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>
GPL-2 | GPL-3
Version
0.2.3

View on CRAN

## Man pages

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