Independent Component Analysis

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)))

}