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")An object of class "ica" which is a list with components
ICA weight matrix
Projected data
Number of iterations
Name of the used function
Learning rate used
Initial weight matrix
The matrix for which the ICA is to be computed
learning rate
number of iterations
number of independent components
function used for the nonlinear computation part
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.