Randomized Singular Value Decomposition
Description
Randomized singular value decomposition (rsvd) is a very fast probabilistic
algorithm to compute an approximated low-rank singular value decomposition of large
data sets with high accuracy. SVD plays a central role in data analysis and scientific computing.
SVD is also widely used for computing principal component analysis (PCA), a linear dimensionality reduction technique.
Randomized PCA (rpca) is using the approximated singular value decomposition to compute the most significant principal components. In addition several plot functions are provided.