HLLE-class {dimRed} | R Documentation |

## Hessian Locally Linear Embedding

### Description

An S4 Class implementing Hessian Locally Linear Embedding (HLLE)

### Details

HLLE uses local hessians to approximate the curvines and is an extension to non-convex subsets in lowdimensional space.

### 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

HLLE can take the following parameters:

- knn
neighborhood size

- ndim
number of output dimensions

### Implementation

Own implementation, sticks to the algorithm in Donoho and Grimes (2003). Makes use of sparsity to speed up final embedding.

### References

Donoho, D.L., Grimes, C., 2003. Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data. PNAS 100, 5591-5596. doi:10.1073/pnas.1031596100

### See Also

Other dimensionality reduction methods:
`AutoEncoder-class`

,
`DRR-class`

,
`DiffusionMaps-class`

,
`DrL-class`

,
`FastICA-class`

,
`FruchtermanReingold-class`

,
`Isomap-class`

,
`KamadaKawai-class`

,
`MDS-class`

,
`NNMF-class`

,
`PCA-class`

,
`PCA_L1-class`

,
`UMAP-class`

,
`dimRedMethod-class`

,
`dimRedMethodList()`

,
`kPCA-class`

,
`nMDS-class`

,
`tSNE-class`

### Examples

```
if(requireNamespace(c("RSpectra", "Matrix", "RANN"), quietly = TRUE)) {
dat <- loadDataSet("3D S Curve", n = 300)
emb <- embed(dat, "HLLE", knn = 15)
plot(emb, type = "2vars")
}
```

*dimRed*version 0.2.6 Index]