Description Usage Arguments Value References Examples
fill.USVT
is a matrix estimation method suitable for low-rank structure. In the context of
our package, we provide this method under the matrix completion problem category. It aims at
exploiting the idea of thresholding the singular values to minimize the mean-squared error, defined as
\mathrm{MSE}(\hat{A}):={E} ≤ft\{ \frac{1}{np} ∑_{i=1}^{n} ∑_{j=1}^{p} (\hat{a}_{ij} - a_{ij})^2 \right\}
where A is an (n\times p) matrix with some missing values and \hat{A} is an estimate.
1 | fill.USVT(A, eta = 0.01)
|
A |
an (n\times p) partially observed matrix. |
eta |
control for thresholding \in (0,1). |
a named list containing
an (n\times p) estimated matrix after completion, which is \hat{A} in the equation above.
chatterjee2015filling
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ## Not run:
## load image data of 'lena128'
data(lena128)
## transform 5% of entries into missing
set.seed(5)
A <- aux.rndmissing(lena128, x=0.05)
## apply the method with 3 different control 'eta'
fill1 <- fill.USVT(A, eta=0.01)
fill2 <- fill.USVT(A, eta=0.5)
fill3 <- fill.USVT(A, eta=0.99)
## visualize only the last ones from each run
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$X, col=gray((0:100)/100), axes=FALSE, main="eta=0.01")
image(fill2$X, col=gray((0:100)/100), axes=FALSE, main="eta=0.5")
image(fill3$X, col=gray((0:100)/100), axes=FALSE, main="eta=0.99")
par(opar)
## End(Not run)
|
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