linear_EXTLPP: Extended Locality Preserving Projection

In Rdimtools: Dimension Reduction and Estimation Methods

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

X	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.

Author(s)

Kisung You

References

\insertRef

shikkenawis_improving_2012Rdimtools

See Also

do.lpp

Examples

## generate data
set.seed(100)
X <- aux.gensamples(n=75)

## 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)

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