Rdimtools (version 1.0.4)

do.cscore: Constraint Score

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.

Usage

do.cscore(
  X,
  label,
  ndim = 2,
  score = c("ratio", "difference"),
  lambda = 0.5,
  preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate")
)

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.

preprocess

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

Value

a named list 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.

trfinfo

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

projection

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

References

zhang_constraint_2008aRdimtools

See Also

do.cscoreg

Examples

# NOT RUN {
## 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.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)
# }
# NOT RUN {
# }