Rdimtools (version 1.1.2)

do.slpp: Supervised Locality Preserving Projection

Description

As its names suggests, Supervised Locality Preserving Projection (SLPP) is a variant of LPP in that it replaces neighborhood network construction schematic with class information in that if two nodes belong to the same class, it assigns weight of 1, i.e., \(S_{ij}=1\) if \(x_i\) and \(x_j\) have same class labelings.

Usage

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

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.

Arguments

X

an \((n\times p)\) matrix or data frame whose rows are observations.

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" and other options of "decorrelate" and "whiten" are supported. See also aux.preprocess for more details.

Author

Kisung You

References

zheng_gabor_2007Rdimtools

See Also

do.lpp

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 SLPP with LPP
outLPP  <- do.lpp(X)
outSLPP <- do.slpp(X, label)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2))
plot(outLPP$Y,  pch=19, col=label, main="LPP")
plot(outSLPP$Y, pch=19, col=label, main="SLPP")
par(opar)