do.cscore | R Documentation |
Constraint Score \insertCitezhang_constraint_2008aRdimtools 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, ...)
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
|
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.
a list containing information for out-of-sample prediction.
name of the algorithm.
do.cscoreg
## 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)
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