do.npe  R Documentation 
do.npe
performs a linear dimensionality reduction using Neighborhood Preserving
Embedding (NPE) proposed by He et al (2005). It can be regarded as a linear approximation
to Locally Linear Embedding (LLE). Like LLE, it is possible for the weight matrix being rank deficient.
If regtype
is set to TRUE
with a proper value of regparam
, it will
perform Tikhonov regularization as designated. When regularization is needed
with regtype
parameter to be FALSE
, it will automatically find a suitable
regularization parameter and put penalty for stable computation. See also
do.lle
for more details.
do.npe( X, ndim = 2, type = c("proportion", 0.1), symmetric = "union", weight = TRUE, preprocess = c("null", "center", "scale", "cscale", "whiten", "decorrelate"), regtype = FALSE, regparam = 1 )
X 
an (n\times p) matrix or data frame whose rows are observations and columns represent independent variables. 
ndim 
an integervalued target dimension. 
type 
a vector of neighborhood graph construction. Following types are supported;

symmetric 
one of 
weight 

preprocess 
an additional option for preprocessing the data.
Default is "null". See also 
regtype 

regparam 
a positive real number for Regularization. Default value is 1. 
a named list containing
an (n\times ndim) matrix whose rows are embedded observations.
a vector of eigenvalues corresponding to basis expansion in an ascending order.
a (p\times ndim) whose columns are basis for projection.
a list containing information for outofsample prediction.
Kisung You
he_neighborhood_2005Rdimtools
## 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 settings for connectivity output1 = do.npe(X, ndim=2, type=c("proportion",0.10)) output2 = do.npe(X, ndim=2, type=c("proportion",0.25)) output3 = do.npe(X, ndim=2, type=c("proportion",0.50)) ## visualize three different projections opar < par(no.readonly=TRUE) par(mfrow=c(1,3)) plot(output1$Y, pch=19, col=label, main="NPE::10% connected") plot(output2$Y, pch=19, col=label, main="NPE::25% connected") plot(output3$Y, pch=19, col=label, main="NPE::50% connected") par(opar) ## End(Not run)
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