Extended Supervised Locality Preserving Projection

Extended LPP and Supervised LPP are two variants of the celebrated Locality Preserving Projection (LPP) algorithm for dimension reduction. Their combination, Extended Supervised LPP, is a combination of two algorithmic novelties in one that it reflects discriminant information with realistic distance measure via Z-score function.

do.eslpp(
  X,
  label,
  ndim = 2,
  numk = max(ceiling(nrow(X)/10), 2),
  preprocess = c("center", "scale", "cscale", "decorrelate", "whiten")
)

Arguments

X: an \((n\times p)\) matrix or data frame whose rows are observations.
label: a length-\(n\) vector of data class labels.
ndim: an integer-valued target dimension.
numk: the number of neighboring points for k-nn graph construction.
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

Zheng Z, Yang F, Tan W, Jia J, Yang J (2007). “Gabor Feature-Based Face Recognition Using Supervised Locality Preserving Projection.” Signal Processing, 87(10), 2473–2483.

Shikkenawis G, Mitra SK (2012). “Improving the Locality Preserving Projection for Dimensionality Reduction.” In 2012 Third International Conference on Emerging Applications of Information Technology, 161–164.

Author

Kisung You

Examples

## generate data of 2 types with clear difference
set.seed(100)
diff = 50
dt1  = aux.gensamples(n=50)-diff;
dt2  = aux.gensamples(n=50)+diff;

## merge the data and create a label correspondingly
Y      = rbind(dt1,dt2)
label  = rep(1:2, each=50)

## compare LPP, SLPP and ESLPP
outLPP   <- do.lpp(Y)
outSLPP  <- do.slpp(Y, label)
outESLPP <- do.eslpp(Y, label)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(outLPP$Y,   col=label, pch=19, main="LPP")
plot(outSLPP$Y,  col=label, pch=19, main="SLPP")
plot(outESLPP$Y, col=label, pch=19, main="ESLPP")

par(opar)