# feature_CSCOREG: Constraint Score using Spectral Graph In Rdimtools: Dimension Reduction and Estimation Methods

 do.cscoreg R Documentation

## Constraint Score using Spectral Graph

### Description

Constraint Score is a filter-type algorithm for feature selection using pairwise constraints. It first marks all pairwise constraints as same- and different-cluster and construct a feature score for both constraints. It takes ratio or difference of feature score vectors and selects the indices with smallest values. Graph laplacian is constructed for approximated nonlinear manifold structure.

### Usage

do.cscoreg(X, label, ndim = 2, score = c("ratio", "difference"), lambda = 0.5)


### Arguments

 X an (n\times p) matrix or data frame whose rows are observations and columns represent independent variables. label a length-n vector of class labels. ndim an integer-valued target dimension. score type of score measures from two score vectors of same- and different-class pairwise constraints; "ratio" and "difference" method. See the paper from the reference for more details. lambda a penalty value for different-class pairwise constraints. Only valid for "difference" scoring method.

### Value

a named Rdimtools S3 object containing

Y

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

cscore

a length-p vector of constraint scores. Indices with smallest values are selected.

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

zhang_constraint_2008aRdimtools

do.cscore

### Examples


## use iris data
## it is known that feature 3 and 4 are more important.
data(iris)
set.seed(100)
subid    = sample(1:150,50)
iris.dat = as.matrix(iris[subid,1:4])
iris.lab = as.factor(iris[subid,5])

## try different strategy
out1 = do.cscoreg(iris.dat, iris.lab, score="ratio")
out2 = do.cscoreg(iris.dat, iris.lab, score="difference", lambda=0)
out3 = do.cscoreg(iris.dat, iris.lab, score="difference", lambda=0.5)
out4 = do.cscoreg(iris.dat, iris.lab, score="difference", lambda=1)

## visualize
plot(out1$Y, pch=19, col=iris.lab, main="ratio") plot(out2$Y, pch=19, col=iris.lab, main="diff/lambda=0")
plot(out3$Y, pch=19, col=iris.lab, main="diff/lambda=0.5") plot(out4$Y, pch=19, col=iris.lab, main="diff/lambda=1")