| ica {e1071} | R Documentation |
This is an R-implementation of the Matlab-Function of Petteri.Pajunen@hut.fi.
For a data matrix X independent components are extracted by applying a
nonlinear PCA algorithm. The parameter fun determines which
nonlinearity is used. fun can either be a function or one of the
following strings "negative kurtosis", "positive kurtosis", "4th
moment" which can be abbreviated to uniqueness. If fun equals
"negative (positive) kurtosis" the function tanh (x-tanh(x)) is used
which provides ICA for sources with negative (positive) kurtosis. For
fun == "4th moments" the signed square function is used.
ica(X, lrate, epochs=100, ncomp=dim(X)[2], fun="negative")
X |
The matrix for which the ICA is to be computed |
lrate |
learning rate |
epochs |
number of iterations |
ncomp |
number of independent components |
fun |
function used for the nonlinear computation part |
An object of class "ica" which is a list with components
weights |
ICA weight matrix |
projection |
Projected data |
epochs |
Number of iterations |
fun |
Name of the used function |
lrate |
Learning rate used |
initweights |
Initial weight matrix |
Currently, there is no reconstruction from the ICA subspace to the original input space.
Andreas Weingessel
Oja et al., ``Learning in Nonlinear Constrained Hebbian Networks'', in Proc. ICANN-91, pp. 385–390.
Karhunen and Joutsensalo, ``Generalizations of Principal Component Analysis, Optimization Problems, and Neural Networks'', Neural Networks, v. 8, no. 4, pp. 549–562, 1995.