Rdimtools (version 1.0.6)

do.npca: Nonnegative Principal Component Analysis

Description

Nonnegative Principal Component Analysis (NPCA) is a variant of PCA where projection vectors - or, basis for learned subspace - contain no negative values.

Usage

do.npca(
  X,
  ndim = 2,
  preprocess = c("center", "scale", "cscale", "decorrelate", "whiten"),
  maxiter = 1000,
  reltol = 1e-05
)

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.

maxiter

number of maximum iteraions allowed.

reltol

stopping criterion for incremental relative error.

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

zafeiriou_nonnegative_2010Rdimtools

See Also

do.pca

Examples

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

## use different preprocessing
out1 = do.npca(X, preprocess="center")
out2 = do.npca(X, preprocess="cscale")
out3 = do.npca(X, preprocess="whiten")

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=label, main="NPCA:: center")
plot(out2$Y, pch=19, col=label, main="NPCA:: cscale")
plot(out3$Y, pch=19, col=label, main="NPCA:: whiten")
par(opar)
# }
# NOT RUN {
# }