do.extlpp: Extended Locality Preserving Projection

Description

Extended Locality Preserving Projection (EXTLPP) is an unsupervised dimension reduction algorithm with a bit of flavor in adopting discriminative idea by nature. It raises a question on the data points at moderate distance in that a Z-shaped function is introduced in defining similarity derived from Euclidean distance.

Usage

do.extlpp(
  X,
  ndim = 2,
  numk = max(ceiling(nrow(X)/10), 2),
  preprocess = c("center", "scale", "cscale", "decorrelate", "whiten")
)

Arguments

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

ndim

an integer-valued target dimension.

numk

the number of neighboring points for k-nn graph construction.

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.

References

shikkenawis_improving_2012Rdimtools

Examples

Run this code

# NOT RUN {
## generate data
X <- aux.gensamples(n=123)

## run Extended LPP with different neighborhood graph
out1 <- do.extlpp(X, numk=5)
out2 <- do.extlpp(X, numk=10)
out3 <- do.extlpp(X, numk=25)

## Visualize three different projections
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, main="EXTLPP::k=5")
plot(out2$Y, main="EXTLPP::k=10")
plot(out3$Y, main="EXTLPP::k=25")
par(opar)
# }
# NOT RUN {
# }