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.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.2.3 by Maciek Sykulski, 5 years ago

Browse source code at

Authors: Maciek Sykulski [aut, cre]

Documentation:   PDF Manual  

Task views: Robust Statistical Methods

GPL-2 | GPL-3 license

Imports compiler

See at CRAN