feature_UDFS: Unsupervised Discriminative Features Selection

In Rdimtools: Dimension Reduction and Estimation Methods

Unsupervised Discriminative Features Selection

Description

Though it may sound weird, this method aims at finding discriminative features under the unsupervised learning framework. It assumes that the class label could be predicted by a linear classifier and iteratively updates its discriminative nature while attaining row-sparsity scores for selecting features.

Usage

do.udfs(
  X,
  ndim = 2,
  lbd = 1,
  gamma = 1,
  k = 5,
  preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate")
)

Arguments

X	an (n\times p) matrix or data frame whose rows are observations and columns represent independent variables.
ndim	an integer-valued target dimension.
lbd	regularization parameter for local Gram matrix to be invertible.
gamma	regularization parameter for row-sparsity via \ell_{2,1} norm.
k	size of nearest neighborhood for each data point.
preprocess	an additional option for preprocessing the data. Default is "null". See also aux.preprocess for more details.

Value

a named list containing

Y

an (n\times ndim) matrix whose rows are embedded observations.

featidx

a length-ndim vector of indices with highest scores.

trfinfo

a list containing information for out-of-sample prediction.

projection

a (p\times ndim) whose columns are basis for projection.

Author(s)

Kisung You

References

\insertRef

yang_l2_2011Rdimtools

Examples

## use iris data
data(iris)
set.seed(100)
subid = sample(1:150, 50)
X     = as.matrix(iris[subid,1:4])
label = as.factor(iris[subid,5])

#### try different neighborhood size
out1 = do.udfs(X, k=5)
out2 = do.udfs(X, k=10)
out3 = do.udfs(X, k=25)

#### visualize
opar = par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=label, main="UDFS::k=5")
plot(out2$Y, pch=19, col=label, main="UDFS::k=10")
plot(out3$Y, pch=19, col=label, main="UDFS::k=25")
par(opar)

Rdimtools documentation built on Dec. 28, 2022, 1:44 a.m.