Randomized singular value decomposition (rsvd) is a very fast probabilistic algorithm that can be used to compute the near optimal low-rank singular value decomposition of massive data sets with high accuracy. SVD plays a central role in data analysis and scientific computing. SVD is also widely used for computing (randomized) principal component analysis (PCA), a linear dimensionality reduction technique. Randomized PCA (rpca) uses the approximated singular value decomposition to compute the most significant principal components. This package also includes a function to compute (randomized) robust principal component analysis (RPCA). In addition several plot functions are provided.
|Author||N. Benjamin Erichson [aut, cre]|
|Date of publication||2016-07-29 06:41:14|
|Maintainer||N. Benjamin Erichson <email@example.com>|
|License||GPL (>= 2)|
ggbiplot: Biplot for 'rPCA' using ggplot2
ggcorplot: Correlation plot
ggscreeplot: Pretty Screeplot
reigen: Randomized Spectral Decomposition of a matrix (reigen).
rpca: Randomized principal component analysis (rpca).
rrpca: Randomized robust principal component analysis (rrpca).
rsvd: Randomized Singular Value Decomposition (rsvd).
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