rsvd: Randomized Singular Value Decomposition
Version 0.6

Randomized singular value decomposition (rsvd) is a very fast probabilistic algorithm that can be used to compute the near optimal low-rank singular value decomposition of massive data sets with high accuracy. SVD plays a central role in data analysis and scientific computing. SVD is also widely used for computing (randomized) principal component analysis (PCA), a linear dimensionality reduction technique. Randomized PCA (rpca) uses the approximated singular value decomposition to compute the most significant principal components. This package also includes a function to compute (randomized) robust principal component analysis (RPCA). In addition several plot functions are provided.

Getting started

Package details

AuthorN. Benjamin Erichson [aut, cre]
Date of publication2016-07-29 06:41:14
MaintainerN. Benjamin Erichson <[email protected]>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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rsvd documentation built on May 29, 2017, 2:28 p.m.