Sparsity Preserving Projection (SPP) is an unsupervised linear dimension reduction technique. It aims to preserve high-dimensional structure in a sparse manner to find projections that keeps such sparsely-connected pattern in the low-dimensional space. Note that we used CVXR for convenient computation, which may lead to slower execution once used for large dataset.
do.spp(
X,
ndim = 2,
preprocess = c("center", "scale", "cscale", "decorrelate", "whiten"),
reltol = 1e-04
)an \((n\times p)\) matrix or data frame whose rows are observations
an integer-valued target dimension.
an additional option for preprocessing the data.
Default is "center". See also aux.preprocess for more details.
tolerance level for stable computation of sparse reconstruction weights.
a named list containing
an \((n\times ndim)\) matrix whose rows are embedded observations.
a list containing information for out-of-sample prediction.
a \((p\times ndim)\) whose columns are basis for projection.
Qiao L, Chen S, Tan X (2010). “Sparsity Preserving Projections with Applications to Face Recognition.” Pattern Recognition, 43(1), 331--341.
if (FALSE) {
## load iris data
data(iris)
set.seed(100)
subid = sample(1:150,50)
X = as.matrix(iris[subid,1:4])
label = as.factor(iris[subid,5])
## test different tolerance levels
out1 <- do.spp(X,ndim=2,reltol=0.001)
out2 <- do.spp(X,ndim=2,reltol=0.01)
out3 <- do.spp(X,ndim=2,reltol=0.1)
# visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=label, main="SPP::reltol=.001")
plot(out2$Y, pch=19, col=label, main="SPP::reltol=.01")
plot(out3$Y, pch=19, col=label, main="SPP::reltol=.1")
par(opar)
}