Randomized Singular Value Decomposition

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.


Reference manual

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0.6 by N. Benjamin Erichson, a year ago


Report a bug at https://github.com/Benli11/rSVD

Browse source code at https://github.com/cran/rsvd

Authors: N. Benjamin Erichson [aut, cre]

Documentation:   PDF Manual  

GPL (>= 2) license

Suggests ggplot2, plyr, scales, grid, testthat, knitr, rmarkdown

Suggested by stm.

See at CRAN