# feature_RSR: Regularized Self-Representation In Rdimtools: Dimension Reduction and Estimation Methods

 do.rsr R Documentation

## 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} + λ \| W \|_{2,1}

where \|W\|_{2,1} = ∑_{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.

Kisung You

### References

\insertRef

zhu_unsupervised_2015Rdimtools

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