Supervised Locality Pursuit Embedding

Supervised Locality Pursuit Embedding (SLPE) is a supervised extension of LPE that uses class labels of data points in order to enhance discriminating power in its mapping into a low dimensional space.

do.slpe(
  X,
  label,
  ndim = 2,
  preprocess = c("center", "scale", "cscale", "decorrelate", "whiten")
)

Arguments

X: an \((n\times p)\) matrix or data frame whose rows are observations and columns represent independent variables.
label: a length-\(n\) vector of data class labels.
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.
trfinfo: a list containing information for out-of-sample prediction.
projection: a \((p\times ndim)\) whose columns are basis for projection.

References

Zheng Z, Yang J (2006). “Supervised Locality Pursuit Embedding for Pattern Classification.” Image and Vision Computing, 24(8), 819--826.

Author

Kisung You

Examples

## use iris data
data(iris)
set.seed(100)
subid = sample(1:150, 50)
X     = as.matrix(iris[subid,1:4])
label = as.factor(iris[subid,5])

## compare SLPE with SLPP
out1 <- do.slpp(X, label)
out2 <- do.slpe(X, label)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2))
plot(out1$Y, pch=19, col=label, main="SLPP")
plot(out2$Y, pch=19, col=label, main="SLPE")

par(opar)