do.rsr {Rdimtools}

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)

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.

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.

Author(s)

Kisung You

References

Zhu P, Zuo W, Zhang L, Hu Q, Shiu SC (2015). “Unsupervised Feature Selection by Regularized Self-Representation.” Pattern Recognition, 48(2), 438–446.

Examples

## 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)