do.spp {Rdimtools}

Sparsity Preserving Projection

Description

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.

Usage

do.spp(
  X,
  ndim = 2,
  preprocess = c("center", "scale", "cscale", "decorrelate", "whiten"),
  reltol = 1e-04
)

Arguments

X

an (n\times p) matrix or data frame whose rows are observations

ndim

an integer-valued target dimension.

preprocess

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

reltol

tolerance level for stable computation of sparse reconstruction weights.

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.

Author(s)

Kisung You

References

Qiao L, Chen S, Tan X (2010). “Sparsity Preserving Projections with Applications to Face Recognition.” Pattern Recognition, 43(1), 331–341.

Examples

## Not run: 
## 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)

## End(Not run)