Rdimtools (version 1.0.6)

do.cscoreg: 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,
  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.cscore

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.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
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,2))
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")
par(opar)

# }