do.sdlpp {Rdimtools}

Sample-Dependent Locality Preserving Projection

Description

Many variants of Locality Preserving Projection are contingent on graph construction schemes in that they sometimes return a range of heterogeneous results when parameters are controlled to cover a wide range of values. This algorithm takes an approach called sample-dependent construction of graph connectivity in that it tries to discover intrinsic structures of data solely based on data.

Usage

do.sdlpp(
  X,
  ndim = 2,
  t = 1,
  preprocess = c("center", "scale", "cscale", "decorrelate", "whiten")
)

Arguments

X

an (n\times p) matrix or data frame whose rows are observations.

ndim

an integer-valued target dimension.

t

kernel bandwidth in (0,\infty).

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.

Author(s)

Kisung You

References

Yang B, Chen S (2010). “Sample-Dependent Graph Construction with Application to Dimensionality Reduction.” Neurocomputing, 74(1-3), 301–314.

See Also

do.lpp

Examples

## use iris data
data(iris)
set.seed(100)
subid = sample(1:150, 50)
X     = as.matrix(iris[subid,1:4])
label = as.factor(iris[subid,5])

## compare with PCA
out1 <- do.pca(X,ndim=2)
out2 <- do.sdlpp(X, t=0.01)
out3 <- do.sdlpp(X, t=10)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=label, main="PCA")
plot(out2$Y, pch=19, col=label, main="SDLPP::t=1")
plot(out3$Y, pch=19, col=label, main="SDLPP::t=10")
par(opar)