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.
funA function that does the embedding and returns a dimRedResult object.
stdparsThe 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:
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. 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
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.