do.cge  R Documentation 
Constrained Graph Embedding (CGE) is a semisupervised embedding method that incorporates partially available label information into the graph structure that find embeddings consistent with the labels.
do.cge( X, label, ndim = 2, type = c("proportion", 0.1), preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate") )
X 
an (n\times p) matrix or data frame whose rows are observations 
label 
a lengthn vector of data class labels. It should contain 
ndim 
an integervalued target dimension. 
type 
a vector of neighborhood graph construction. Following types are supported;

preprocess 
an additional option for preprocessing the data.
Default is 
a named list containing
an (n\times ndim) matrix whose rows are embedded observations.
a list containing information for outofsample prediction.
Kisung You
he_graph_2009Rdimtools
## use iris data data(iris) X = as.matrix(iris[,2:4]) label = as.integer(iris[,5]) lcols = as.factor(label) ## copy a label and let 10% of elements be missing nlabel = length(label) nmissing = round(nlabel*0.10) label_missing = label label_missing[sample(1:nlabel, nmissing)]=NA ## try different neighborhood sizes out1 = do.cge(X, label_missing, type=c("proportion",0.10)) out2 = do.cge(X, label_missing, type=c("proportion",0.25)) out3 = do.cge(X, label_missing, type=c("proportion",0.50)) ## visualize opar = par(no.readonly=TRUE) par(mfrow=c(1,3)) plot(out1$Y, main="10% connected", pch=19, col=lcols) plot(out2$Y, main="25% connected", pch=19, col=lcols) plot(out3$Y, main="50% connected", pch=19, col=lcols) par(opar)
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