| do.nolpp | R Documentation |
Nonnegative Orthogonal Locality Preserving Projection (NOLPP) is a variant of OLPP where projection vectors - or, basis for learned subspace - contain no negative values.
do.nolpp(
X,
ndim = 2,
type = c("proportion", 0.1),
preprocess = c("null", "center", "scale", "cscale", "decorrelate", "whiten"),
t = 1,
maxiter = 1000,
reltol = 1e-05
)
X |
an |
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 "null". See also |
t |
kernel bandwidth in |
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.olpp
## 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 kernel bandwidths with 20% connectivity
out1 = do.nolpp(X, type=c("proportion",0.5), t=0.01)
out2 = do.nolpp(X, type=c("proportion",0.5), t=0.1)
out3 = do.nolpp(X, type=c("proportion",0.5), t=1)
## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, col=label, main="NOLPP::t=0.01")
plot(out2$Y, col=label, main="NOLPP::t=0.1")
plot(out3$Y, col=label, main="NOLPP::t=1")
par(opar)
## End(Not run)
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