# feature_LASSO: Least Absolute Shrinkage and Selection Operator In Rdimtools: Dimension Reduction and Estimation Methods

 do.lasso R Documentation

## Least Absolute Shrinkage and Selection Operator

### Description

LASSO is a popular regularization scheme in linear regression in pursuit of sparsity in coefficient vector that has been widely used. The method can be used in feature selection in that given the regularization parameter, it first solves the problem and takes indices of estimated coefficients with the largest magnitude as meaningful features by solving

\textrm{min}_{β} ~ \frac{1}{2}\|Xβ-y\|_2^2 + λ \|β\|_1

where y is response in our method.

### Usage

do.lasso(X, response, ndim = 2, lambda = 1)


### Arguments

 X an (n\times p) matrix whose rows are observations and columns represent independent variables. response a length-n vector of response variable. ndim an integer-valued target dimension. lambda sparsity regularization parameter in (0,∞).

### 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

tibshirani_regression_1996Rdimtools

### Examples


## generate swiss roll with auxiliary dimensions
## it follows reference example from LSIR paper.
set.seed(1)
n = 123
theta = runif(n)
h     = runif(n)
t     = (1+2*theta)*(3*pi/2)
X     = array(0,c(n,10))
X[,1] = t*cos(t)
X[,2] = 21*h
X[,3] = t*sin(t)
X[,4:10] = matrix(runif(7*n), nrow=n)

## corresponding response vector
y = sin(5*pi*theta)+(runif(n)*sqrt(0.1))

## try different regularization parameters
out1 = do.lasso(X, y, lambda=0.1)
out2 = do.lasso(X, y, lambda=1)
out3 = do.lasso(X, y, lambda=10)

## visualize
plot(out1$Y, main="LASSO::lambda=0.1") plot(out2$Y, main="LASSO::lambda=1")