Rdimtools (version 0.3.2)

do.eslpp: Extended Supervised Locality Preserving Projection

Description

Extended LPP and Supervised LPP are two variants of the celebrated Locality Preserving Projection (LPP) algorithm for dimension reduction. Their combination, Extended Supervised LPP, is a combination of two algorithmic novelties in one that it reflects discriminant information with realistic distance measure via Z-score function.

Usage

do.eslpp(X, label, 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.

label

a length-\(n\) vector of data class labels.

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

zheng_gabor_2007Rdimtools

shikkenawis_improving_2012Rdimtools

See Also

do.lpp, do.slpp, do.extlpp

Examples

# NOT RUN {
## generate data of 2 types with clear difference
diff = 15
dt1  = aux.gensamples(n=123)-diff;
dt2  = aux.gensamples(n=123)+diff;

## merge the data and create a label correspondingly
Y      = rbind(dt1,dt2)
label  = c(rep(1,123), rep(2,123))

## compare LPP, SLPP and ESLPP
outLPP  <- do.lpp(Y)
outSLPP <- do.slpp(Y, label)
outESLPP <- do.eslpp(Y, label)

## visualize
par(mfrow=c(1,3))
plot(outLPP$Y[,1], outLPP$Y[,2], main="LPP")
plot(outSLPP$Y[,1], outSLPP$Y[,2], main="SLPP")
plot(outESLPP$Y[,1], outESLPP$Y[,2], main="ESLPP")
# }
# NOT RUN {
# }