R: Randomized robust principal component analysis (rrpca).

rrpca {rsvd}

R Documentation

Randomized robust principal component analysis (rrpca).

Description

Robust principal components analysis separates a matrix into a low-rank plus sparse component.

Usage

rrpca(A, lambda = NULL, maxiter = 50, tol = 1e-05, p = 10, q = 2,
  trace = FALSE, rand = TRUE)

Arguments

`A`	array_like; a real (m, n) input matrix (or data frame) to be decomposed. na.omit is applied, if the data contain NAs.
`lambda`	scalar, optional; tuning parameter (default lambda = max(m,n)^-0.5).
`maxiter`	integer, optional; maximum number of iterations (default maxiter = 20).
`tol`	scalar, optional; precision parameter (default tol = 1.0e-5).
`p`	integer, optional; oversampling parameter for rsvd (default p=10), see `rsvd`.
`q`	integer, optional; number of additional power iterations for rsvd (default q=2), see `rsvd`.
`trace`	bool, optional; print progress.
`rand`	bool, optional; if (TRUE), the rsvd routine is used, otherwise svd is used.

Details

Robust principal component analysis (RPCA) is a method for the robust seperation of a a rectangular (m,n) matrix A into a low-rank component L and a sparse comonent S:

A = L + S

To decompose the matrix, we use the inexact augmented Lagrange multiplier method (IALM). The algorithm can be used in combination with either the randomized or deterministic SVD.

Value

rrpca returns a list containing the following components:

`L`	array_like; low-rank component; (m, n) dimensional array.
`S`	array_like sparse component; (m, n) dimensional array.

Author(s)

N. Benjamin Erichson, erichson@uw.edu

References

[1] Lin, Zhouchen, Minming Chen, and Yi Ma. "The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices." (2010). (available at arXiv http://arxiv.org/abs/1009.5055).
[2] N. B. Erichson, S. Voronin, S. Brunton, J. N. Kutz. "Randomized matrix decompositions using R" (2016). (available at 'arXiv http://arxiv.org/abs/1608.02148).

Examples

library('rsvd')

# Create toy video
# background frame
xy <- seq(-50, 50, length.out=100)
mgrid <- list( x=outer(xy*0,xy,FUN="+"), y=outer(xy,xy*0,FUN="+") )
bg <- 0.1*exp(sin(-mgrid$x**2-mgrid$y**2))
toyVideo <- matrix(rep(c(bg), 100), 100*100, 100)

# add moving object
for(i in 1:90) {
  mobject <- matrix(0, 100, 100)
  mobject[i:(10+i), 45:55] <- 0.2
  toyVideo[,i] =  toyVideo[,i] + c( mobject )
}

# Foreground/Background separation
out <- rrpca(toyVideo, trace=TRUE)

# Display results of the seperation for the 10th frame
par(mfrow=c(1,4))
image(matrix(bg, ncol=100, nrow=100)) #true background
image(matrix(toyVideo[,10], ncol=100, nrow=100)) # frame
image(matrix(out$L[,10], ncol=100, nrow=100)) # seperated background
image(matrix(out$S[,10], ncol=100, nrow=100)) #seperated foreground

[Package rsvd version 0.9 Index]