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rpca

RobustPCA

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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.

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Version 0.2.3
Published 2015-07-31 3356 days ago
Needs compilation? no
License GPL-2
License GPL-3
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Maintainer

Maintainer

Maciek Sykulski

Authors

Maciek Sykulski

aut / cre

Material

Reference manual
Package source

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Robust

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arm64

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arm64

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x86_64

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Windows

r-devel

x86_64

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x86_64

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Imports

compiler