fill.SVT {filling} | R Documentation |
Singular Value Thresholding for Nuclear Norm Optimization
Description
fill.SVT
is an iterative updating scheme for Nuclear Norm Minimization problem. An unconstrained
parahrase of the problem introduced in fill.nuclear
is
where if it is observed, or
otherwise.
It performs iterative shrinkage on newly computed singular values.
Usage
fill.SVT(A, lambda = 1, maxiter = 100, tol = 0.001)
Arguments
A |
an |
lambda |
a regularization parameter. |
maxiter |
maximum number of iterations to be performed. |
tol |
stopping criterion for an incremental progress. |
Value
a named list containing
- X
an
matrix after completion.
References
Cai J, Candès EJ, Shen Z (2010). “A Singular Value Thresholding Algorithm for Matrix Completion.” SIAM Journal on Optimization, 20(4), 1956–1982. ISSN 1052-6234, 1095-7189.
See Also
Examples
## Not run:
## load image data of 'lena128'
data(lena128)
## transform 5% of entries into missing
A <- aux.rndmissing(lena128, x=0.05)
## apply the method
fill1 <- fill.SVT(A, lambda=0.1)
fill2 <- fill.SVT(A, lambda=1.0)
fill3 <- fill.SVT(A, lambda=20)
## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,2), pty="s")
image(A, col=gray((0:100)/100), axes=FALSE, main="5% missing")
image(fill1$X, col=gray((0:100)/100), axes=FALSE, main="lbd=0.1")
image(fill2$X, col=gray((0:100)/100), axes=FALSE, main="lbd=1")
image(fill3$X, col=gray((0:100)/100), axes=FALSE, main="lbd=10")
par(opar)
## End(Not run)
[Package filling version 0.2.3 Index]