| do.nonpp | R Documentation |
Nonnegative Orthogonal Neighborhood Preserving Projections (NONPP) is a variant of ONPP where projection vectors - or, basis for learned subspace - contain no negative values.
do.nonpp(
X,
ndim = 2,
type = c("proportion", 0.1),
preprocess = c("null", "center", "decorrelate", "whiten"),
maxiter = 1000,
reltol = 1e-05
)
X |
an (n\times p) matrix or data frame whose rows are observations. |
ndim |
an integer-valued target dimension. |
type |
a vector of neighborhood graph construction. Following types are supported;
|
preprocess |
an additional option for preprocessing the data.
Default is "center" and other options of "decorrelate" and "whiten"
are supported. See also |
maxiter |
number of maximum iteraions allowed. |
reltol |
stopping criterion for incremental relative error. |
a named list containing
an (n\times ndim) matrix whose rows are embedded observations.
a list containing information for out-of-sample prediction.
a (p\times ndim) whose columns are basis for projection.
Kisung You
zafeiriou_nonnegative_2010Rdimtools
do.onpp
## 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])
## use different levels of connectivity
out1 = do.nonpp(X, type=c("proportion",0.1))
out2 = do.nonpp(X, type=c("proportion",0.2))
out3 = do.nonpp(X, type=c("proportion",0.5))
## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, col=label, main="NONPP::10% connected")
plot(out2$Y, col=label, main="NONPP::20% connected")
plot(out3$Y, col=label, main="NONPP::50% connected")
par(opar)
## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.