R: Independent Component Analysis

ica {e1071} | R Documentation

## Independent Component Analysis

### Description

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.

### 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

`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.

[Package e1071 version 1.7-4 Index]