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.
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
Note
Currently, there is no reconstruction from the ICA subspace to the
original input space.
Author(s)
Andreas Weingessel
References
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.