ica: Independent Component Analysis

In e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien

Independent Component Analysis

Description

This is an R-implementation of the Matlab-Function of [e-mail].

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.

e1071 documentation built on Sept. 17, 2024, 1:06 a.m.