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.
an \((n\times p)\) matrix or data frame whose rows are observations and columns represent independent variables.
a length-\(n\) vector of data class labels.
an integer-valued target dimension.
an additional option for preprocessing the data.
Default is "center". See also aux.preprocess
for more details.
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.
one of "intersect"
, "union"
or "asymmetric"
is supported. Default is "union"
. See also aux.graphnbd
for more details.
TRUE
to use local scaling method for construction affinity matrix, FALSE
for binary affinity.
a named list containing
an \((n\times ndim)\) matrix whose rows are embedded observations.
a \((p\times ndim)\) whose columns are basis for projection.
a list containing information for out-of-sample prediction.
Sugiyama M (2006). “Local Fisher Discriminant Analysis for Supervised Dimensionality Reduction.” In Proceedings of the 23rd International Conference on Machine Learning, 905--912.
Zelnik-manor L, Perona P (2005). “Self-Tuning Spectral Clustering.” In Saul LK, Weiss Y, Bottou L (eds.), Advances in Neural Information Processing Systems 17, 1601--1608. MIT Press.
## 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)