do.disr: Diversity-Induced Self-Representation

Description

Diversity-Induced Self-Representation (DISR) is a feature selection method that aims at ranking features by both representativeness and diversity. Self-representation controlled by lbd1 lets the most representative features to be selected, while lbd2 penalizes the degree of inter-feature similarity to enhance diversity from the chosen features.

Usage

do.disr(
  X,
  ndim = 2,
  preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate"),
  lbd1 = 1,
  lbd2 = 1
)

Arguments

an \((n\times p)\) matrix or data frame whose rows are observations and columns represent independent variables.

ndim

an integer-valued target dimension.

preprocess

an additional option for preprocessing the data. Default is "null". See also aux.preprocess for more details.

lbd1

nonnegative number to control the degree of self-representation.

lbd2

nonnegative number to control the degree of feature similarity.

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

liu_unsupervised_2017Rdimtools

Examples

Run this code

# NOT RUN {
## 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 lbd combinations
out1 = do.disr(X, lbd1=1, lbd2=1)
out2 = do.disr(X, lbd1=1, lbd2=5)
out3 = do.disr(X, lbd1=5, lbd2=1)
out4 = do.disr(X, lbd1=5, lbd2=5)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,2))
plot(out1$Y, main="(lbd1,lbd2)=(1,1)", col=label, pch=19)
plot(out2$Y, main="(lbd1,lbd2)=(1,5)", col=label, pch=19)
plot(out3$Y, main="(lbd1,lbd2)=(5,1)", col=label, pch=19)
plot(out4$Y, main="(lbd1,lbd2)=(5,5)", col=label, pch=19)
par(opar)
# }
# NOT RUN {
# }