Laplacian Eigenmaps
An S4 Class implementing Laplacian Eigenmaps
Laplacian Eigenmaps use a kernel and were originally developed to separate non-convex clusters under the name spectral clustering.
funA function that does the embedding and returns a dimRedResult object.
stdparsThe standard parameters for the function.
Dimensionality reduction methods are S4 Classes that either be used
directly, in which case they have to be initialized and a full
list with parameters has to be handed to the @fun()
slot, or the method name be passed to the embed function and
parameters can be given to the ..., in which case
missing parameters will be replaced by the ones in the
@stdpars.
LaplacianEigenmaps can take the following parameters:
the number of output dimensions.
A character vector specifying hot to make the graph
sparse, "knn" means that a K-nearest neighbor graph is
constructed, "eps" an epsilon neighborhood graph is
constructed, else a dense distance matrix is used.
The number of nearest neighbors to use for the knn graph.
The distance for the epsilon neighborhood graph.
Parameter for the transformation of the distance matrix
by w=exp(-d^2/t), larger values give less weight to
differences in distance, t == Inf treats all distances != 0 equally.
logical, should the normed laplacian be used?
Wraps around spec.emb.
Belkin, M., Niyogi, P., 2003. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Computation 15, 1373.
dat <- loadDataSet("3D S Curve")
leim <- LaplacianEigenmaps()
emb <- leim@fun(dat, leim@stdpars)
plot(emb@data@data)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.