Package: rpca 0.3.2

rpca: RobustPCA: Decompose a Matrix into Low-Rank and Sparse Components, truncated version, with additional L2 noise separation option

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.

Authors:Maciek Sykulski [aut, cre], Krzysztof Gogolewski [aut]

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rpca.pdf |rpca.html
rpca/json (API)

# Install 'rpca' in R:
install.packages('rpca', repos = c('https://macieksk.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/macieksk/rpca/issues

On CRAN:

3.67 score 3 stars 31 scripts 214 downloads 1 mentions 5 exports 3 dependencies

Last updated 3 years agofrom:197df1b727. Checks:OK: 1 WARNING: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 11 2024
R-4.5-winWARNINGOct 11 2024
R-4.5-linuxWARNINGOct 11 2024
R-4.4-winWARNINGOct 11 2024
R-4.4-macWARNINGOct 11 2024
R-4.3-winWARNINGOct 11 2024
R-4.3-macWARNINGOct 11 2024

Exports:F2normrpcathresh.l1thresh.nucleartrpca

Dependencies:irlbalatticeMatrix