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 |
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.
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 |
Karhunen and Joutsensalo, "Generalizations of Principal Component Analysis, Optimization Problems, and Neural Networks", Neural Networks, v. 8, no. 4, pp. 549-562, 1995.