fill_SoftImpute: SoftImpute : Spectral Regularization

In filling: Matrix Completion, Imputation, and Inpainting Methods

Description

fill.SoftImpute implements convex relaxation techniques to generate a sequence of regularized low-rank solutions for matrix completion problems. For the nuclear norm optimization problem, it uses soft thresholding technique iteratively in that the algorithm returns several matrices in accordance with the provided vector of regularization parameters λ (lambdas).

Usage

fill.SoftImpute(A, lambdas = c(10, 1, 0.1), maxiter = 100, tol = 0.001)

Arguments

A	an (n\times p) partially observed matrix.
lambdas	a length-t vector regularization parameters.
maxiter	maximum number of iterations to be performed.
tol	stopping criterion for an incremental progress.

Value

a named list containing

X

an (n\times p\times t) cubic array after completion at each lambda value.

References

\insertRef

mazumder_spectral_2010filling

See Also

fill.HardImpute

Examples

## load image data of 'lena128'
data(lena128)

## transform 5% of entries into missing
A <- aux.rndmissing(lena128, x=0.05)

## apply the method with 3 lambda values
fill <- fill.SoftImpute(A, lambdas=c(500,100,50))

## 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(fill$X[,,1], col=gray((0:100)/100), axes=FALSE, main="lambda=500")
image(fill$X[,,2], col=gray((0:100)/100), axes=FALSE, main="lambda=100")
image(fill$X[,,3], col=gray((0:100)/100), axes=FALSE, main="lambda=50")
par(opar)

filling documentation built on Aug. 21, 2021, 5:09 p.m.