Rdimtools (version 1.0.2)

do.mvp: Maximum Variance Projection

Description

Maximum Variance Projection (MVP) is a supervised method based on linear discriminant analysis (LDA). In addition to classical LDA, it further aims at preserving local information by capturing the local geometry of the manifold via the following proximity coding, $$S_{ij}=1\text{if}{C}_i\ne C_j\text{and}=0\text{otherwise}$$, where $C_i$ is the label of an $i$-th data point.

Usage

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

Arguments

X

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

label

a length-$n$ vector of data class labels.

ndim

an integer-valued target dimension.

preprocess

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

projection

a $(p\times ndim)$ whose columns are basis for projection.

References

zhang_maximum_2007Rdimtools

Examples

# NOT RUN {
## use 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])

## perform MVP with different preprocessings
out1 = do.mvp(X, label)
out2 = do.mvp(X, label, preprocess="decorrelate")
out3 = do.mvp(X, label, preprocess="whiten")

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, col=label, pch=19, main="centering")
plot(out2$Y, col=label, pch=19, main="decorrelating")
plot(out3$Y, col=label, pch=19, main="whitening")
par(opar)

# }