Rdimtools (version 1.1.2)

do.lfda: Local Fisher Discriminant Analysis

Description

Local Fisher Discriminant Analysis (LFDA) is a linear dimension reduction method for supervised case, i.e., labels are given. It reflects local information to overcome undesired results of traditional Fisher Discriminant Analysis which results in a poor mapping when samples in a single class form form several separate clusters.

Usage

do.lfda(
  X,
  label,
  ndim = 2,
  preprocess = c("center", "scale", "cscale", "decorrelate", "whiten"),
  type = c("proportion", 0.1),
  symmetric = c("union", "intersect", "asymmetric"),
  localscaling = TRUE
)

Value

a named list containing

Y

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

projection

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

trfinfo

a list containing information for out-of-sample prediction.

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.

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.

symmetric

one of "intersect", "union" or "asymmetric" is supported. Default is "union". See also aux.graphnbd for more details.

localscaling

TRUE to use local scaling method for construction affinity matrix, FALSE for binary affinity.

Author

Kisung You

References

sugiyama_local_2006Rdimtools

zelnik-manor_selftuning_2005Rdimtools

Examples

## generate 3 different groups of data X and label vector
x1 = matrix(rnorm(4*10), nrow=10)-20
x2 = matrix(rnorm(4*10), nrow=10)
x3 = matrix(rnorm(4*10), nrow=10)+20
X     = rbind(x1, x2, x3)
label = rep(1:3, each=10)

## try different affinity matrices
out1 = do.lfda(X, label)
out2 = do.lfda(X, label, localscaling=FALSE)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2))
plot(out1$Y, col=label, main="binary affinity matrix")
plot(out2$Y, col=label, main="local scaling affinity")
par(opar)