Rdimtools (version 0.3.2)

do.lspp: Local Similarity Preserving Projection

Description

Local Similarity Preserving Projection (LSPP) is a variant of LPP in that it employs a sample-dependent graph generation process as of do.sdlpp. LSPP takes advantage of labeling information to correct local similarity weight in order to make intra-class weight larger than inter-class weight. It uses PCA preprocessing as suggested from the original work.

Usage

do.lspp(X, label, 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.

label

a length-\(n\) vector of data class labels.

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

huang_local_2015Rdimtools

See Also

do.sdlpp, do.lpp

Examples

# NOT RUN {
## generate data of 2 types with clear difference
diff = 15
dt1  = aux.gensamples(n=123)-diff;
dt2  = aux.gensamples(n=123)+diff;

## merge the data and create a label correspondingly
Y      = rbind(dt1,dt2)
label  = c(rep(1,123), rep(2,123))

## compare with PCA
out1 <- do.pca(Y, ndim=2)
out2 <- do.slpp(Y, label, ndim=2)

## visualize
par(mfrow=c(1,2))
plot(out1$Y[,1], out1$Y[,2], main="PCA")
plot(out2$Y[,1], out2$Y[,2], main="LSPP")

# }