do.slpp {Rdimtools}

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"))

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.

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

Zheng Z, Yang F, Tan W, Jia J, Yang J (2007). “Gabor Feature-Based Face Recognition Using Supervised Locality Preserving Projection.” Signal Processing, 87(10), 2473–2483.

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)