Conventional LTSA method relies on PCA for approximating local tangent spaces. Improved LTSA (ILTSA) provides a remedy that can efficiently recover the geometric structure of data manifolds even when data are sparse or non-uniformly distributed.
an \((n\times p)\) matrix or data frame whose rows are observations and columns represent independent variables.
an integer-valued target dimension.
a vector of neighborhood graph construction. Following types are supported;
c("knn",k)
, c("enn",radius)
, and c("proportion",ratio)
.
Default is c("proportion",0.1)
, connecting about 1/10 of nearest data points
among all data points. See also aux.graphnbd
for more details.
one of "intersect"
, "union"
or "asymmetric"
is supported. Default is "union"
. See also aux.graphnbd
for more details.
an additional option for preprocessing the data.
Default is "center". See also aux.preprocess
for more details.
heat kernel bandwidth parameter in \((0,\infty)\).
a named list containing
an \((n\times ndim)\) matrix whose rows are embedded observations.
a list containing information for out-of-sample prediction.
Zhang P, Qiao H, Zhang B (2011). “An Improved Local Tangent Space Alignment Method for Manifold Learning.” Pattern Recognition Letters, 32(2), 181--189.
# \donttest{
## load 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])
## try different bandwidth size
out1 <- do.iltsa(X, t=1)
out2 <- do.iltsa(X, t=10)
out3 <- do.iltsa(X, t=100)
## Visualize two comparisons
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=label, main="ILTSA::t=1")
plot(out2$Y, pch=19, col=label, main="ILTSA::t=10")
plot(out3$Y, pch=19, col=label, main="ILTSA::t=100")
par(opar)
# }