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.

Version: 0.2.3
Imports: compiler
Published: 2015-07-31
Author: Maciek Sykulski [aut, cre]
Maintainer: Maciek Sykulski <macieksk at gmail.com>
License: GPL-2 | GPL-3
NeedsCompilation: no
CRAN checks: rpca results

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Reference manual: rpca.pdf
Package source: rpca_0.2.3.tar.gz
Windows binaries: r-devel: rpca_0.2.3.zip, r-release: rpca_0.2.3.zip, r-oldrel: rpca_0.2.3.zip
OS X El Capitan binaries: r-release: rpca_0.2.3.tgz
OS X Mavericks binaries: r-oldrel: rpca_0.2.3.tgz

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