Package: rpca
Type: Package
Title: RobustPCA: Decompose a Matrix into Low-Rank and Sparse
        Components, truncated version, with additional L2 noise
        separation option
Version: 0.3.2
Date: 2018-01-11
Author: Maciek Sykulski [aut, cre], Krzysztof Gogolewski [aut]
Maintainer: Maciek Sykulski <macieksk@gmail.com>
Authors@R: c(person("Maciek", "Sykulski", email = "macieksk@gmail.com", role = c("aut", "cre")), 
             person("Krzysztof", "Gogolewski", role = "aut"))
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.
License: GPL-2 | GPL-3
Imports: compiler, irlba, Matrix
Repository: https://macieksk.r-universe.dev
Date/Publication: 2021-10-03 12:48:52 UTC
RemoteUrl: https://github.com/macieksk/rpca
RemoteRef: HEAD
RemoteSha: 197df1b727818ccd920db04d03e9d2a5446596ec
NeedsCompilation: no
Packaged: 2026-10-01 09:04:26 UTC; root