Randomized Singular Value Decomposition

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.


Reference manual

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1.0.5 by N. Benjamin Erichson, 6 months ago


Report a bug at https://github.com/erichson/rSVD/issues

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

Authors: N. Benjamin Erichson [aut, cre]

Documentation:   PDF Manual  

GPL (>= 3) license

Imports Matrix

Suggests ggplot2, testthat

Imported by LRQMM, LSX, TCA, jackstraw, sparsepca.

Suggested by Seurat, stm.

See at CRAN