do.lspe | R Documentation |
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.
do.lspe( X, ndim = 2, preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate"), alpha = 1, beta = 1, bandwidth = 1 )
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 |
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. |
a named list containing
an (n\times ndim) matrix whose rows are embedded observations.
a length-ndim vector of indices with highest scores.
a list containing information for out-of-sample prediction.
a (p\times ndim) whose columns are basis for projection.
Kisung You
fang_locality_2014Rdimtools
do.rsr
#### generate R12in72 dataset set.seed(100) X = aux.gensamples(n=50, 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)
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