FastICA-class {dimRed} | R Documentation |

## Independent Component Analysis

### Description

An S4 Class implementing the FastICA algorithm for Indepentend
Component Analysis.

### Details

ICA is used for blind signal separation of different sources. It is
a linear Projection.

### 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

FastICA can take the following parameters:

- ndim
The number of output dimensions. Defaults to `2`

### Implementation

Wraps around `fastICA`

. FastICA uses a very
fast approximation for negentropy to estimate statistical
independences between signals. Because it is a simple
rotation/projection, forward and backward functions can be given.

### References

Hyvarinen, A., 1999. Fast and robust fixed-point algorithms for independent
component analysis. IEEE Transactions on Neural Networks 10, 626-634.
https://doi.org/10.1109/72.761722

### See Also

Other dimensionality reduction methods:
`AutoEncoder-class`

,
`DRR-class`

,
`DiffusionMaps-class`

,
`DrL-class`

,
`FruchtermanReingold-class`

,
`HLLE-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("fastICA", quietly = TRUE)) {
dat <- loadDataSet("3D S Curve")
emb <- embed(dat, "FastICA", ndim = 2)
plot(getData(getDimRedData(emb)))
}
```

[Package

*dimRed* version 0.2.6

Index]