rsvd: Randomized Singular Value Decomposition
Version 0.9

Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided.

Getting started

Package details

AuthorN. Benjamin Erichson [aut, cre]
Date of publication2017-12-08 18:54:07 UTC
MaintainerN. Benjamin Erichson <[email protected]>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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rsvd documentation built on Dec. 9, 2017, 1:03 a.m.