| do.nrsr | R Documentation |
In the standard, convex RSR problem (do.rsr), row-sparsity for self-representation is
acquired using matrix \ell_{2,1} norm, i.e, \|W\|_{2,1} = \sum \|W_{i:}\|_2. Its non-convex
extension aims at achieving higher-level of sparsity using arbitrarily chosen \|W\|_{2,l} norm for
l\in (0,1) and this exploits Iteratively Reweighted Least Squares (IRLS) algorithm for computation.
do.nrsr(
X,
ndim = 2,
expl = 0.5,
preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate"),
lbd = 1
)
X |
an |
ndim |
an integer-valued target dimension. |
expl |
an exponent in |
preprocess |
an additional option for preprocessing the data.
Default is "null". See also |
lbd |
nonnegative number to control the degree of self-representation by imposing row-sparsity. |
a named list containing
an (n\times ndim) matrix whose rows are embedded observations.
a length-ndim vector of indices with highest scores.
a list containing information for out-of-sample prediction.
a (p\times ndim) whose columns are basis for projection.
Kisung You
zhu_nonconvex_2017Rdimtools
do.rsr
## use iris data
data(iris)
set.seed(100)
subid = sample(1:150, 50)
X = as.matrix(iris[subid,1:4])
label = as.factor(iris[subid,5])
#### try different exponents for regularization
out1 = do.nrsr(X, expl=0.01)
out2 = do.nrsr(X, expl=0.1)
out3 = do.nrsr(X, expl=0.5)
#### visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=label, main="NRSR::expl=0.01")
plot(out2$Y, pch=19, col=label, main="NRSR::expl=0.1")
plot(out3$Y, pch=19, col=label, main="NRSR::expl=0.5")
par(opar)
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