do.disr {Rdimtools}

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

X

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 regularization of the self-representation.

lbd2

nonnegative number to control the degree of feature diversity. lbd2=1 gives equal weight to self-representation and diversity.

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

Liu Y, Liu K, Zhang C, Wang J, Wang X (2017). “Unsupervised Feature Selection via Diversity-Induced Self-Representation.” Neurocomputing, 219, 350–363.

See Also

do.rsr

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 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)