Rdimtools (version 0.3.2)

do.elpp2: Enhanced Locality Preserving Projection (2013)

Description

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.

Usage

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_enhanced_2013Rdimtools

See Also

do.pflpp

Examples

# NOT RUN {
## generate swiss roll data
X = aux.gensamples(n=200)

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

## visualize
par(mfrow=c(1,3))
plot(out1$Y[,1], out1$Y[,2], main="PCA")
plot(out2$Y[,1], out2$Y[,2], main="Parameter-Free LPP")
plot(out3$Y[,1], out3$Y[,2], main="Enhanced LPP (2013)")
# }
# NOT RUN {
# }