do.lsdf  R Documentation 
Locality Sensitive Discriminant Feature (LSDF) is a semisupervised feature selection method. It utilizes both labeled and unlabeled data points in that labeled points are used to maximize the margin between data opints from different classes, while labeled ones are used to discover the geometrical structure of the data space.
do.lsdf( X, label, ndim = 2, type = c("proportion", 0.1), preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate"), gamma = 100 )
X 
an (n\times p) matrix or data frame whose rows are observations and columns represent independent variables. 
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 "null". See also 
gamma 
withinclass weight parameter for sameclass data. 
a named list containing
an (n\times ndim) matrix whose rows are embedded observations.
a lengthndim vector of indices with highest scores.
a list containing information for outofsample prediction.
a (p\times ndim) whose columns are basis for projection.
Kisung You
cai_locality_2007Rdimtools
## generate data of 3 types with clear difference set.seed(100) dt1 = aux.gensamples(n=20)50 dt2 = aux.gensamples(n=20) dt3 = aux.gensamples(n=20)+50 ## merge the data and create a label correspondingly X = rbind(dt1,dt2,dt3) label = rep(1:3, each=20) ## copy a label and let 20% of elements be missing nlabel = length(label) nmissing = round(nlabel*0.20) label_missing = label label_missing[sample(1:nlabel, nmissing)]=NA ## try different neighborhood sizes out1 = do.lsdf(X, label_missing, type=c("proportion",0.10)) out2 = do.lsdf(X, label_missing, type=c("proportion",0.25)) out3 = do.lsdf(X, label_missing, type=c("proportion",0.50)) ## visualize opar < par(no.readonly=TRUE) par(mfrow=c(1,3)) plot(out1$Y, pch=19, col=label, main="10% connectivity") plot(out2$Y, pch=19, col=label, main="25% connectivity") plot(out3$Y, pch=19, col=label, main="50% connectivity") par(opar)
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