linear_LQMI: Linear Quadratic Mutual Information

In Rdimtools: Dimension Reduction and Estimation Methods

Linear Quadratic Mutual Information

Description

Linear Quadratic Mutual Information (LQMI) is a supervised linear dimension reduction method. Quadratic Mutual Information is an efficient nonparametric estimation method for Mutual Information for class labels not requiring class priors. For the KQMI formulation, LQMI is a linear equivalent.

Usage

do.lqmi(
  X,
  label,
  ndim = 2,
  preprocess = c("center", "scale", "cscale", "whiten", "decorrelate")
)

Arguments

X	an (n\times p) matrix or data frame whose rows are observations and columns represent independent variables.
label	a length-n vector of data class labels.
ndim	an integer-valued target dimension.
preprocess	an additional option for preprocessing the data. Default is "center". See also aux.preprocess for more details.

Value

a named list containing

Y

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

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

bouzas_graph_2015Rdimtools

See Also

do.kqmi

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])

## compare against LDA
out1 = do.lda(X, label)
out2 = do.lqmi(X, label)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2))
plot(out1$Y, col=label, main="LDA projection")
plot(out2$Y, col=label, main="LQMI projection")
par(opar)

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