| do.elde | R Documentation |
Local Discriminant Embedding (LDE) suffers from a small-sample-size problem where scatter matrix may suffer from rank deficiency. Exponential LDE (ELDE) provides not only a remedy for the problem using matrix exponential, but also a flexible framework to transform original data into a new space via distance diffusion mapping similar to kernel-based nonlinear mapping.
do.elde(
X,
label,
ndim = 2,
t = 1,
preprocess = c("center", "scale", "cscale", "decorrelate", "whiten"),
k1 = max(ceiling(nrow(X)/10), 2),
k2 = max(ceiling(nrow(X)/10), 2)
)
X |
an |
label |
a length- |
ndim |
an integer-valued target dimension. |
t |
kernel bandwidth in |
preprocess |
an additional option for preprocessing the data.
Default is "center". See also |
k1 |
the number of same-class neighboring points (homogeneous neighbors). |
k2 |
the number of different-class neighboring points (heterogeneous neighbors). |
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.
Kisung You
dornaika_exponential_2013Rdimtools
do.lde
## generate data of 3 types with difference
set.seed(100)
dt1 = aux.gensamples(n=20)-50
dt2 = aux.gensamples(n=20)
dt3 = aux.gensamples(n=20)+50
## merge the data and create a label correspondingly
X = rbind(dt1,dt2,dt3)
label = rep(1:3, each=20)
## try different kernel bandwidth
out1 = do.elde(X, label, t=1)
out2 = do.elde(X, label, t=10)
out3 = do.elde(X, label, t=100)
## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=label, main="ELDE::bandwidth=1")
plot(out2$Y, pch=19, col=label, main="ELDE::bandwidth=10")
plot(out3$Y, pch=19, col=label, main="ELDE::bandwidth=100")
par(opar)
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