Constraint Score

Constraint Score (Zhang et al. 2008) 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.

do.cscore(X, label, ndim = 2, ...)

Arguments

X

an \((n\times p)\) matrix whose rows are observations and columns represent independent variables.

label

a length-\(n\) vector of class labels.

ndim

an integer-valued target dimension (default: 2).

...

extra parameters including

preprocess

an additional option for preprocessing the data. See also aux.preprocess for more details (default: "null").

score

type of score measures from two score vectors of same- and different-class pairwise constraints; "ratio" (default) 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. (default: 0.5).

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.

trfinfo

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

algorithm

name of the algorithm.

References

Zhang D, Chen S, Zhou Z (2008). “Constraint Score: A New Filter Method for Feature Selection with Pairwise Constraints.” Pattern Recognition, 41(5), 1440--1451.

See also

do.cscoreg

Examples

# \donttest{
## use iris data
## it is known that feature 3 and 4 are more important.
data(iris)
iris.dat = as.matrix(iris[,1:4])
iris.lab = as.factor(iris[,5])

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

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,2))
plot(out1$Y, col=iris.lab, main="ratio")
plot(out2$Y, col=iris.lab, main="diff/lambda=0")
plot(out3$Y, col=iris.lab, main="diff/lambda=0.5")
plot(out4$Y, col=iris.lab, main="diff/lambda=1")

par(opar)
# }