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rpca  

RobustPCA: Decompose a Matrix into Low-Rank and Sparse Components
View on CRAN: Click here


Download and install rpca package within the R console
Install from CRAN:
install.packages("rpca")

Install from Github:
library("remotes")
install_github("cran/rpca")

Install by package version:
library("remotes")
install_version("rpca", "0.2.3")



Attach the package and use:
library("rpca")
Maintained by
Maciek Sykulski
[Scholar Profile | Author Map]
First Published: 2015-07-31
Latest Update: 2015-07-31
Description:
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.
How to cite:
Maciek Sykulski (2015). rpca: RobustPCA: Decompose a Matrix into Low-Rank and Sparse Components. R package version 0.2.3, https://cran.r-project.org/web/packages/rpca. Accessed 26 Mar. 2025.
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Complete documentation for rpca
Functions, R codes and Examples using the rpca R package
Some associated functions: F2norm . rpca-package . rpca . thresh.l1 . thresh.nuclear . 
Some associated R codes: robustpca.R .  Full rpca package functions and examples
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