rsvd: Randomized Singular Value Decomposition

Computes low-rank matrix approximations for dimension reduction, data analysis, and compression. Randomized algorithms use random weighted combinations of the input to find a small subspace, then factorize the projected matrix. This can reduce the work when the requested rank is small relative to the matrix dimensions and the input has a useful low-rank approximation. Methods include singular value decomposition, principal component analysis, QB factorization, and interpolative and CUR decompositions. Robust principal component analysis separates a matrix into low-rank and entrywise sparse components. Selected routines support sparse matrices, including principal component analysis with implicit centering and scaling. Plotting functions display scores, loadings, and explained variance. See Erichson et al. (2019) <doi:10.18637/jss.v089.i11> for the algorithms and examples of their use.

install.packages("rsvd")

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