do.lltsa {Rdimtools} | R Documentation |
Linear Local Tangent Space Alignment
Description
Linear Local Tangent Space Alignment (LLTSA) is a linear variant of the celebrated LTSA method. It uses the tangent space in the neighborhood for each data point to represent the local geometry. Alignment of those local tangent spaces in the low-dimensional space returns an explicit mapping from the high-dimensional space.
Usage
do.lltsa(
X,
ndim = 2,
type = c("proportion", 0.1),
symmetric = c("union", "intersect", "asymmetric"),
preprocess = c("center", "scale", "cscale", "decorrelate", "whiten")
)
Arguments
X |
an |
ndim |
an integer-valued target dimension. |
type |
a vector of neighborhood graph construction. Following types are supported;
|
symmetric |
one of |
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
Zhang T, Yang J, Zhao D, Ge X (2007). “Linear Local Tangent Space Alignment and Application to Face Recognition.” Neurocomputing, 70(7-9), 1547–1553.
See Also
Examples
## use iris dataset
data(iris)
set.seed(100)
subid = sample(1:150,50)
X = as.matrix(iris[subid,1:4])
lab = as.factor(iris[subid,5])
## try different neighborhood size
out1 <- do.lltsa(X, type=c("proportion",0.25))
out2 <- do.lltsa(X, type=c("proportion",0.50))
out3 <- do.lltsa(X, type=c("proportion",0.75))
## Visualize three different projections
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, col=lab, pch=19, main="LLTSA::25% connected")
plot(out2$Y, col=lab, pch=19, main="LLTSA::50% connected")
plot(out3$Y, col=lab, pch=19, main="LLTSA::75% connected")
par(opar)