Locality Preserving Fisher Discriminant Analysis (LPFDA) is a supervised variant of LPP. It can also be seemed as an improved version of LDA where the locality structure of the data is preserved. The algorithm aims at getting a subspace projection matrix by solving a generalized eigenvalue problem.
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.
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.
an additional option for preprocessing the data.
Default is "center". See also aux.preprocess
for more details.
bandwidth parameter for heat kernel in \((0,\infty)\).
a named list containing
an \((n\times ndim)\) matrix whose rows are embedded observations.
a list containing information for out-of-sample prediction.
a \((p\times ndim)\) whose columns are basis for projection.
Zhao X, Tian X (2009). “Locality Preserving Fisher Discriminant Analysis for Face Recognition.” In Huang D, Jo K, Lee H, Kang H, Bevilacqua V (eds.), Emerging Intelligent Computing Technology and Applications, 261--269.
## generate data of 3 types with clear difference
set.seed(100)
dt1 = aux.gensamples(n=20)-50
dt2 = aux.gensamples(n=20)
dt3 = aux.gensamples(n=20)+50
## merge the data and create a label correspondingly
X = rbind(dt1,dt2,dt3)
label = rep(1:3, each=20)
## try different proportion of connected edges
out1 = do.lpfda(X, label, type=c("proportion",0.10))
out2 = do.lpfda(X, label, type=c("proportion",0.25))
out3 = do.lpfda(X, label, type=c("proportion",0.50))
## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=label, main="10% connectivity")
plot(out2$Y, pch=19, col=label, main="25% connectivity")
plot(out3$Y, pch=19, col=label, main="50% connectivity")
par(opar)