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

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

Examples

Run this code

# 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
par(mfrow=c(1,3))
plot(out1$Y[,1], out1$Y[,2], main="LSPE::bandwidth=0.1")
plot(out2$Y[,1], out2$Y[,2], main="LSPE::bandwidth=1")
plot(out3$Y[,1], out3$Y[,2], main="LSPE::bandwidth=10")
# }
# NOT RUN {
# }