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.

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.

## 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.

Kisung You

## Examples

# \donttest{
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
plot(out1$Y, pch=19, col=label, main="RSR::lbd=0.1") plot(out2$Y, pch=19, col=label, main="RSR::lbd=1")