Rdimtools (version 0.3.2)

do.olda: Orthogonal Linear Discriminant Analysis

Description

Orthogonal LDA (OLDA) is an extension of classical LDA where the discriminant vectors are orthogonal to each other.

Usage

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

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

ye_characterization_2005Rdimtools

Examples

# NOT RUN {
## generate data of 3 types with clear difference
dt1  = aux.gensamples(n=33)-100
dt2  = aux.gensamples(n=33)
dt3  = aux.gensamples(n=33)+100

## merge the data and create a label correspondingly
X      = rbind(dt1,dt2,dt3)
label  = c(rep(1,33), rep(2,33), rep(3,33))

## compare with LDA
out1 = do.lda(X, label)
out2 = do.olda(X, label)

## visualize
par(mfrow=c(1,2))
plot(out1$Y[,1], out1$Y[,2], main="LDA")
plot(out2$Y[,1], out2$Y[,2], main="Orthogonal LDA")
# }
# NOT RUN {
# }