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

`fun`

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

`stdpars`

The standard parameters for the function.

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`

.

FastICA can take the following parameters:

- ndim
The number of output dimensions. Defaults to

`2`

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.

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

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

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`

# NOT RUN { 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) # }