Enhanced Locality Preserving Projection (2013)

Enhanced Locality Preserving Projection proposed in 2013 (ELPP2) is built upon a parameter-free philosophy from PFLPP. It further aims to exclude its projection to be uncorrelated in the sense that the scatter matrix is placed in a generalized eigenvalue problem.

do.elpp2(
  X,
  ndim = 2,
  preprocess = c("center", "scale", "cscale", "decorrelate", "whiten")
)

Arguments

X: an \((n\times p)\) matrix or data frame whose rows are observations
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.
projection: a \((p\times ndim)\) whose columns are basis for projection.
trfinfo: a list containing information for out-of-sample prediction.

References

Dornaika F, Assoum A (2013). “Enhanced and Parameterless Locality Preserving Projections for Face Recognition.” Neurocomputing, 99, 448--457.

Author

Kisung You

Examples

## use iris data
data(iris)
set.seed(100)
subid = sample(1:150,50)
X     = as.matrix(iris[subid,1:4])
lab   = as.factor(iris[subid,5])

## compare with PCA and PFLPP
out1 = do.pca(X, ndim=2)
out2 = do.pflpp(X, ndim=2)
out3 = do.elpp2(X, ndim=2)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=lab, main="PCA")
plot(out2$Y, pch=19, col=lab, main="Parameter-Free LPP")
plot(out3$Y, pch=19, col=lab, main="Enhanced LPP (2013)")

par(opar)