Rdimtools (version 0.4.1)

do.udfs: 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.

References

yang_l21-norm_2011Rdimtools

Examples

# NOT RUN {
#### generate R12in72 dataset
X = aux.gensamples(n=50, dname="R12in72")

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

#### visualize
par(mfrow=c(1,3))
plot(out1$Y[,1], out1$Y[,2], main="UDFS::k=5")
plot(out2$Y[,1], out2$Y[,2], main="UDFS::k=10")
plot(out3$Y[,1], out3$Y[,2], main="UDFS::k=25")
# }
# NOT RUN {
# }