FastICA-class {dimRed} | R Documentation |
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.
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:
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.
Other dimensionality reduction methods: DRR-class
,
DiffusionMaps-class
,
DrL-class
,
FruchtermanReingold-class
,
HLLE-class
, Isomap-class
,
KamadaKawai-class
, LLE-class
,
MDS-class
, PCA-class
,
dimRedMethod-class
,
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)