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.

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

## 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.

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 W, Wang Z, Sun C (2015). “A Collaborative Representation Based Projections Method for Feature Extraction.” Pattern Recognition, 48(1), 20--27.

do.spp

Kisung You

## Examples

## 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
plot(out1$Y, col=lab, pch=19, main="CRP::lambda=0.1") plot(out2$Y, col=lab, pch=19, main="CRP::lambda=1")