FastICA-class {dimRed}R Documentation

Independent Component Analysis


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


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



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


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.


FastICA can take the following parameters:


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.


Hyvarinen, A., 1999. Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks 10, 626-634.

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


if(requireNamespace("fastICA", quietly = TRUE)) {

dat <- loadDataSet("3D S Curve")
emb <- embed(dat, "FastICA", ndim = 2)


[Package dimRed version 0.2.6 Index]