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]