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)
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