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 |

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
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