Lowrank 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 largescale 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. The methods are discussed in detail by Erichson et al. (2016) <arXiv:1608.02148>.
Package details 


Author  N. Benjamin Erichson [aut, cre] 
Maintainer  N. Benjamin Erichson <[email protected]> 
License  GPL (>= 3) 
Version  1.0.0 
URL  https://github.com/erichson/rSVD 
Package repository  View on CRAN 
Installation 
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