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.

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.

## References

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

do.lpp

Kisung You

## 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
plot(out1$Y, pch=19, col=label, main="PCA") plot(out2$Y, pch=19, col=label, main="SDLPP::t=1")