Rdimtools (version 1.1.2)

do.rsr: Regularized Self-Representation

Description

Given a data matrix \(X\) where observations are stacked in a row-wise manner, Regularized Self-Representation (RSR) aims at finding a solution to following optimization problem $$\textrm{min}~ \|X-XW\|_{2,1} + \lambda \| W \|_{2,1}$$ where \(\|W\|_{2,1} = \sum_{i=1}^{m} \|W_{i:} \|_2\) is an \(\ell_{2,1}\) norm that imposes row-wise sparsity constraint.

Usage

do.rsr(X, ndim = 2, lbd = 1)

Value

a named Rdimtools S3 object containing

Y

an \((n\times ndim)\) matrix whose rows are embedded observations.

featidx

a length-\(ndim\) vector of indices with highest scores.

projection

a \((p\times ndim)\) whose columns are basis for projection.

algorithm

name of the algorithm.

Arguments

X

an \((n\times p)\) matrix or data frame whose rows are observations and columns represent independent variables.

ndim

an integer-valued target dimension.

lbd

nonnegative number to control the degree of self-representation by imposing row-sparsity.

Author

Kisung You

References

zhu_unsupervised_2015Rdimtools

Examples

# \donttest{
## load 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 lbd combinations
out1 = do.rsr(X, lbd=0.1)
out2 = do.rsr(X, lbd=1)
out3 = do.rsr(X, lbd=10)

#### visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=label, main="RSR::lbd=0.1")
plot(out2$Y, pch=19, col=label, main="RSR::lbd=1")
plot(out3$Y, pch=19, col=label, main="RSR::lbd=10")
par(opar)
# }