do.mcfs  R Documentation 
MultiCluster Feature Selection (MCFS) is an unsupervised feature selection method. Based on a multicluster assumption, it aims at finding meaningful features using sparse reconstruction of spectral basis using LASSO.
do.mcfs( X, ndim = 2, type = c("proportion", 0.1), preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate"), K = max(round(nrow(X)/5), 2), lambda = 1, t = 10 )
X 
an (n\times p) matrix or data frame whose rows are observations and columns represent independent variables. 
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 
K 
assumed number of clusters in the original dataset. 
lambda 
\ell_1 regularization parameter in (0,∞). 
t 
bandwidth parameter for heat kernel in (0,∞). 
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_unsupervised_2010Rdimtools
## generate data of 3 types with clear difference dt1 = aux.gensamples(n=20)100 dt2 = aux.gensamples(n=20) dt3 = aux.gensamples(n=20)+100 ## merge the data and create a label correspondingly X = rbind(dt1,dt2,dt3) label = rep(1:3, each=20) ## try different regularization parameters out1 = do.mcfs(X, lambda=0.01) out2 = do.mcfs(X, lambda=0.1) out3 = do.mcfs(X, lambda=1) ## visualize opar < par(no.readonly=TRUE) par(mfrow=c(1,3)) plot(out1$Y, pch=19, col=label, main="lambda=0.01") plot(out2$Y, pch=19, col=label, main="lambda=0.1") plot(out3$Y, pch=19, col=label, main="lambda=1") par(opar)
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