do.cscoreg | R Documentation |
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.
do.cscoreg(X, label, ndim = 2, score = c("ratio", "difference"), lambda = 0.5)
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; |
lambda |
a penalty value for different-class pairwise constraints. Only valid for |
a named Rdimtools
S3 object containing
an (n\times ndim) matrix whose rows are embedded observations.
a length-p vector of constraint scores. Indices with smallest values are selected.
a length-ndim vector of indices with highest scores.
a (p\times ndim) whose columns are basis for projection.
name of the algorithm.
Kisung You
zhang_constraint_2008aRdimtools
do.cscore
## 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)
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