LaplacianEigenmaps-class {dimRed} R Documentation

## Laplacian Eigenmaps

### Description

An S4 Class implementing Laplacian Eigenmaps

### Details

Laplacian Eigenmaps use a kernel and were originally developed to separate non-convex clusters under the name spectral clustering.

### Slots

fun

A function that does the embedding and returns a dimRedResult object.

stdpars

The standard parameters for the function.

### General usage

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.

### Parameters

LaplacianEigenmaps can take the following parameters:

ndim

the number of output dimensions.

sparse

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.

knn

The number of nearest neighbors to use for the knn graph.

eps

The distance for the epsilon neighborhood graph.

t

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.

norm

logical, should the normed laplacian be used?

### Implementation

Wraps around spec.emb.

### References

Belkin, M., Niyogi, P., 2003. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Computation 15, 1373.

### Examples

if(requireNamespace(c("loe", "RSpectra", "Matrix"), quietly = TRUE)) {

dat <- loadDataSet("3D S Curve")
emb <- embed(dat, "LaplacianEigenmaps")
plot(emb@data@data)

}


[Package dimRed version 0.2.6 Index]