# ica

##### Independent Component Analysis

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.

- Keywords
- multivariate

##### Usage

`ica(X, lrate, epochs=100, ncomp=dim(X)[2], fun="negative")`

##### Arguments

- 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

##### Value

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

##### Note

Currently, there is no reconstruction from the ICA subspace to the original input space.

##### 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.

*Documentation reproduced from package e1071, version 1.7-3, License: GPL-2 | GPL-3*