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`, `LLE-class`, `MDS-class`, `NNMF-class`, `PCA-class`, `PCA_L1-class`, `UMAP-class`, `dimRedMethod-class`, `dimRedMethodList`, `kPCA-class`, `nMDS-class`, `tSNE-class`

### Examples

    dat <- loadDataSet("3D S Curve")

    ## use the S4 Class directly:
    fastica <- FastICA()
    emb <- fastica@fun(dat, pars = list(ndim = 2))

    ## simpler, use embed():
    emb2 <- embed(dat, "FastICA", ndim = 2)

    plot(emb@data@data)

[Package dimRed version 0.2.2 Index]