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Fast Randomized Singular Value Decomposition using R

Randomized singular value decomposition (rsvd) is a fast probabilistic algorithm that can be used to compute the near optimal low-rank singular value decomposition of massive data sets with high accuracy. The key idea is to compute a compressed representation of the data to capture the essential information. This compressed representation can then be used to obtain the low-rank singular value decomposition decomposition. The rsvd package provides one of the fastest routines for low-rank matrix approximations in R, as far as we know. The computational advantage becomes pronounced with an increasing matrix dimension (here target-rank k=50):


The singular value decomposition plays a central role in data analysis and scientific computing. The 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. See for further details: “Randomized Matrix Decompositions using R”.

SVD example: Image compression


# Image compression using randomized SVD
s <- rsvd(tiger, k=150) = s$u %*% diag(s$d) %*% t(s$v) # reconstruct image

# Display orginal and reconstrucuted image
image(tiger, col = gray((0:255)/255))
image(, col = gray((0:255)/255))

Here are the results: tiger

and the speedup gained over the base SVD function:


timing_svd <- microbenchmark(
  'SVD' = svd(tiger, nu=150, nv=150),
  'rSVD' = rsvd(tiger, k=150),

print(timing_svd, unit='s')



Install the rsvd package via CRAN


You can also install the development version from GitHub using devtools:


The source packge can be obtained here: CRAN: rsvd.

New in Version 1.0.0


Benli11/rPCA documentation built on Nov. 6, 2018, 10:46 p.m.