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 1.5-2

Index]