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. The methods are discussed in detail by
Erichson et al. (2016) arXiv:1608.02148.