linear_KUDP: Kernel-Weighted Unsupervised Discriminant Projection
In Rdimtools: Dimension Reduction and Estimation Methods

do.kudp

R Documentation

Kernel-Weighted Unsupervised Discriminant Projection

Description

Kernel-Weighted Unsupervised Discriminant Projection (KUDP) is a generalization of UDP where proximity is given by weighted values via heat kernel,

K_{i,j} = \exp(-\|x_i-x_j\|^2/bandwidth)

whence UDP uses binary connectivity. If bandwidth is +∞, it becomes a standard UDP problem. Like UDP, it also performs PCA preprocessing for rank-deficient case.

Usage

do.kudp(
  X,
  ndim = 2,
  type = c("proportion", 0.1),
  preprocess = c("center", "scale", "cscale", "decorrelate", "whiten"),
  bandwidth = 1
)

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.
`type`	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.
`preprocess`	an additional option for preprocessing the data. Default is "center". See also `aux.preprocess` for more details.
`bandwidth`	bandwidth parameter for heat kernel as the equation above.

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.
interimdim: the number of PCA target dimension used in preprocessing.

Author(s)

Kisung You

References

\insertRef

yang_globally_2007Rdimtools

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])

## use different kernel bandwidth
out1 <- do.kudp(X, bandwidth=0.1)
out2 <- do.kudp(X, bandwidth=10)
out3 <- do.kudp(X, bandwidth=1000)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, col=lab, pch=19, main="bandwidth=0.1")
plot(out2$Y, col=lab, pch=19, main="bandwidth=10")
plot(out3$Y, col=lab, pch=19, main="bandwidth=1000")
par(opar)

Rdimtools documentation built on Dec. 28, 2022, 1:44 a.m.