Suppose we have a data matrix, which is the superposition of a lowrank 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 lowrank 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.
Package details 


Author  Maciek Sykulski [aut, cre] 
Date of publication  20150731 01:15:38 
Maintainer  Maciek Sykulski <macieksk@gmail.com> 
License  GPL2  GPL3 
Version  0.2.3 
Package repository  View on CRAN 
Installation 
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