Rdimtools (version 1.0.0)

do.lspe: Locality and Similarity Preserving Embedding

Description

Locality and Similarity Preserving Embedding (LSPE) is a feature selection method based on Neighborhood Preserving Embedding (do.npe) and Sparsity Preserving Projection (do.spp) by first building a neighborhood graph and then mapping the locality structure to reconstruct coefficients such that data similarity is preserved. Use of ${\ell }_{2,1}$ norm boosts to impose column-sparsity that enables feature selection procedure.

Usage

do.lspe(
  X,
  ndim = 2,
  preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate"),
  alpha = 1,
  beta = 1,
  bandwidth = 1
)

Arguments

X

an $(n\times p)$ matrix or data frame whose rows are observations and columns represent independent variables.

ndim

an integer-valued target dimension.

preprocess

an additional option for preprocessing the data. Default is "null". See also aux.preprocess for more details.

alpha

nonnegative number to control ${\ell }_{2,1}$ norm of projection.

beta

nonnegative number to control the degree of local similarity.

bandwidth

positive number for Gaussian kernel bandwidth to define similarity.

Value

a named list containing

Y

an $(n\times ndim)$ matrix whose rows are embedded observations.

featidx

a length-$ndim$ vector of indices with highest scores.

trfinfo

a list containing information for out-of-sample prediction.

projection

a $(p\times ndim)$ whose columns are basis for projection.

References

fang_locality_2014Rdimtools

See Also

do.rsr

Examples

# NOT RUN {
#### generate R12in72 dataset
X = aux.gensamples(dname="R12in72")

#### try different bandwidth values
out1 = do.lspe(X, bandwidth=0.1)
out2 = do.lspe(X, bandwidth=1)
out3 = do.lspe(X, bandwidth=10)

#### visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, main="LSPE::bandwidth=0.1")
plot(out2$Y, main="LSPE::bandwidth=1")
plot(out3$Y, main="LSPE::bandwidth=10")
par(opar)
# }
# NOT RUN {
# }