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.
an \((n\times p)\) matrix or data frame whose rows are observations.
an integer-valued target dimension.
the number of neighboring points for k-nn graph construction.
an additional option for preprocessing the data.
Default is "center". See also aux.preprocess
for more details.
a named list containing
an \((n\times ndim)\) matrix whose rows are embedded observations.
a list containing information for out-of-sample prediction.
a \((p\times ndim)\) whose columns are basis for projection.
Shikkenawis G, Mitra SK (2012). “Improving the Locality Preserving Projection for Dimensionality Reduction.” In 2012 Third International Conference on Emerging Applications of Information Technology, 161--164.
## 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)