# ica

0th

Percentile

##### 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

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.

##### 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.

##### Aliases
• ica
• plot.ica
• print.ica
Documentation reproduced from package e1071, version 1.7-3, License: GPL-2 | GPL-3

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