Rdimtools (version 0.3.2)

do.sdlpp: 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.

References

yang_sample-dependent_2010Rdimtools

See Also

do.lpp

Examples

# NOT RUN {
## generate data
X <- aux.gensamples(n=200,dname="crown")

## compare with PCA
out1 <- do.pca(X,ndim=2)
out2 <- do.sdlpp(X, t=0.1)
out3 <- do.sdlpp(X, t=1)
out4 <- do.sdlpp(X, t=10.0)

## visualize
par(mfrow=c(2,2))
plot(out1$Y[,1], out1$Y[,2], main="PCA")
plot(out2$Y[,1], out2$Y[,2], main="SDLPP; t=0.1")
plot(out3$Y[,1], out3$Y[,2], main="SDLPP; t=1")
plot(out4$Y[,1], out4$Y[,2], main="SDLPP; t=10")
# }
# NOT RUN {

# }