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.
Documentation:
Downloads:
Reverse dependencies:
| Reverse imports: |
ADImpute, BiocSingular, FPCdpca, harf, jackstraw, MUGS, nlpembeds, scRecover, slalom, sparsepca, SPECK, TCA, text2map, wordvector |
| Reverse suggests: |
eigencore, flashier, LSX, MAST, multivarious, scds, Seurat, stm |
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