do.lamp: Local Affine Multidimensional Projection

Description

Local Affine Mulditimensional Projection (LAMP) can be considered as a nonlinear method even though each datam is projected using locally estimated affine mapping. It first finds a low-dimensional embedding for control points and then locates the rest data using affine mapping. We use \(\sqrt{n}\) number of data as controls and Stochastic Neighborhood Embedding is applied as an initial projection of control set. Note that this belongs to the method for visualization so projection onto \(\mathbf{R}^2\) is suggested for use.

Usage

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

Arguments

an \((n\times p)\) matrix or data frame whose rows are observations and columns represent independent variables.

ndim

an integer-valued target dimension.

preprocess

an additional option for preprocessing the data. Default is "null". 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.

References

joia_local_2011Rdimtools

Examples

Run this code

# NOT RUN {
## 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])

## let's compare with PCA
out1 <- do.pca(X, ndim=2)      # PCA
out2 <- do.lamp(X, ndim=2)     # LAMP

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

# }