do.ugfs | R Documentation |
UGFS is an unsupervised feature selection method with two parameters nbdk
and varthr
that it constructs
an affinity graph using local variance computation and scores variables based on PageRank algorithm.
do.ugfs( X, ndim = 2, nbdk = 5, varthr = 2, preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate") )
X |
an (n\times p) matrix or data frame whose rows are observations and columns represent independent variables. |
ndim |
an integer-valued target dimension. |
nbdk |
the size of neighborhood for local variance computation. |
varthr |
threshold value for affinity graph construction. If too small so that the graph of variables is not constructed, it returns an error. |
preprocess |
an additional option for preprocessing the data. Default is "null". See also |
a named list containing
an (n\times ndim) matrix whose rows are embedded observations.
a length-p vector of score computed from PageRank algorithm. Indices with largest values are selected.
a length-ndim vector of indices with highest scores.
a list containing information for out-of-sample prediction.
a (p\times ndim) whose columns are basis for projection.
Kisung You
henni_unsupervised_2018Rdimtools
## 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 multiple thresholding values out1 = do.ugfs(iris.dat, nbdk=10, varthr=0.5) out2 = do.ugfs(iris.dat, nbdk=10, varthr=5.0) out3 = do.ugfs(iris.dat, nbdk=10, varthr=9.5) ## visualize opar <- par(no.readonly=TRUE) par(mfrow=c(1,3)) plot(out1$Y, pch=19, col=iris.lab, main="bandwidth=0.1") plot(out2$Y, pch=19, col=iris.lab, main="bandwidth=1") plot(out3$Y, pch=19, col=iris.lab, main="bandwidth=10") par(opar)
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