Rdimtools (version 0.3.2)

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

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.

References

shikkenawis_improving_2012Rdimtools

See Also

do.lpp

Examples

# 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
par(mfrow=c(1,3))
plot(out1$Y[,1], out1$Y[,2], main="k=5")
plot(out2$Y[,1], out2$Y[,2], main="k=10")
plot(out3$Y[,1], out3$Y[,2], main="k=25")

# }