Local Affine Mulditimensional Projection (LAMP) can be considered as a nonlinear method even though each datum 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.

do.lamp(X, ndim = 2)

## Arguments

X

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

ndim

an integer-valued target dimension.

## Value

a named Rdimtools S3 object containing

Y

an $$(n\times ndim)$$ matrix whose rows are embedded observations.

algorithm

name of the algorithm.

## References

Joia P, Paulovich FV, Coimbra D, Cuminato JA, Nonato LG (2011). “Local Affine Multidimensional Projection.” IEEE Transactions on Visualization and Computer Graphics, 17(12), 2563--2571.

Kisung You

## Examples

# \donttest{
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
plot(out1$Y, pch=19, col=label, main="PCA") plot(out2$Y, pch=19, col=label, main="LAMP")