Rdimtools (version 0.4.1)

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, preprocess = c("null", "center", "scale", "cscale",
  "whiten", "decorrelate"), 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.

preprocess

an additional option for preprocessing the data. Default is "null". See also aux.preprocess for more details.

lbd

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

Value

a named list containing

Y

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

featidx

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

trfinfo

a list containing information for out-of-sample prediction.

projection

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

References

zhu_unsupervised_2015Rdimtools

Examples

# NOT RUN {
#### generate R12in72 dataset
X = aux.gensamples(n=100, dname="R12in72")

#### try different lbd combinations
out1 = do.rsr(X, lbd=0.1)
out2 = do.rsr(X, lbd=1)
out3 = do.rsr(X, lbd=10)

#### visualize
par(mfrow=c(1,3))
plot(out1$Y[,1], out1$Y[,2], main="RSR::lbd=0.1")
plot(out2$Y[,1], out2$Y[,2], main="RSR::lbd=1")
plot(out3$Y[,1], out3$Y[,2], main="RSR::lbd=10")
# }
# NOT RUN {
# }