do.crp: Collaborative Representation-based Projection

Description

Collaborative Representation-based Projection (CRP) is an unsupervised linear dimension reduction method. Its embedding is based on \(\ell\)_2 graph construction, similar to that of SPP where sparsity constraint is imposed via \(\ell_1\) optimization problem. Note that though it may be way faster, rank deficiency can pose a great deal of problems, especially when the dataset is large.

Usage

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

Arguments

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.

lambda

regularization parameter for constructing \(\ell_2\) graph.

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.

References

yang_collaborative_2015Rdimtools

Examples

Run this code

# NOT RUN {
## use iris dataset
data(iris)
set.seed(100)
subid = sample(1:150,50)
X     = as.matrix(iris[subid,1:4])
lab   = as.factor(iris[subid,5])

## test different regularization parameters
out1 <- do.crp(X,ndim=2,lambda=0.1)
out2 <- do.crp(X,ndim=2,lambda=1)
out3 <- do.crp(X,ndim=2,lambda=10)

# visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, col=lab, pch=19, main="CRP::lambda=0.1")
plot(out2$Y, col=lab, pch=19, main="CRP::lambda=1")
plot(out3$Y, col=lab, pch=19, main="CRP::lambda=10")
par(opar)

# }