do.slpe {Rdimtools} | R Documentation |
Supervised Locality Pursuit Embedding
Description
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.
Usage
do.slpe(
X,
label,
ndim = 2,
preprocess = c("center", "scale", "cscale", "decorrelate", "whiten")
)
Arguments
X |
an |
label |
a length- |
ndim |
an integer-valued target dimension. |
preprocess |
an additional option for preprocessing the data.
Default is "center". See also |
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.
Author(s)
Kisung You
References
Zheng Z, Yang J (2006). “Supervised Locality Pursuit Embedding for Pattern Classification.” Image and Vision Computing, 24(8), 819–826.
See Also
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)