do.dspp {Rdimtools}

Discriminative Sparsity Preserving Projection

Description

Discriminative Sparsity Preserving Projection (DSPP) is a supervised dimension reduction method that employs sparse representation model to adaptively build both intrinsic adjacency graph and penalty graph. It follows an integration of global within-class structure into manifold learning under exploiting discriminative nature provided from label information.

Usage

do.dspp(
  X,
  label,
  ndim = 2,
  preprocess = c("center", "scale", "cscale", "decorrelate", "whiten"),
  lambda = 1,
  rho = 1
)

Arguments

X

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

label

a length-n vector of data class labels.

ndim

an integer-valued target dimension.

preprocess

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

lambda

regularization parameter for constructing sparsely weighted network.

rho

a parameter for balancing the local and global contribution.

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

Gao Q, Huang Y, Zhang H, Hong X, Li K, Wang Y (2015). “Discriminative Sparsity Preserving Projections for Image Recognition.” Pattern Recognition, 48(8), 2543–2553.

Examples

## Not run: 
## use 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])

## try different rho values
out1 <- do.dspp(X, label, ndim=2, rho=0.01)
out2 <- do.dspp(X, label, ndim=2, rho=0.1)
out3 <- do.dspp(X, label, ndim=2, rho=1)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, main="rho=0.01", col=label, pch=19)
plot(out2$Y, main="rho=0.1",  col=label, pch=19)
plot(out3$Y, main="rho=1",    col=label, pch=19)
par(opar)

## End(Not run)