JADE (version 2.0-4)

FOBI: Function to perform FOBI for ICA

Description

The FOBI method for independent component analysis (ICA). We assume that all components have different kurtosis values.

Usage

FOBI(X, na.action = na.fail)

Value

A list with class 'bss' containing the following components:

W

estimated unmixing matrix.

EV

eigenvectors of autocovariance matrix.

Xmu

the original mean of the data.

S

estimated sources as time series objected standardized to have mean 0 and unit variances.

Arguments

X

a numeric matrix.

na.action

A function which indicates what should happen when the data contain 'NA's. Default is to fail.

Author

Klaus Nordhausen

References

Cardoso, J.-F. (1989), Source separation using higher order moments, in Proceedings of IEEE International Conference on Accoustics, Speech and Signal Processing, 2109--2112.

Miettinen, J., Taskinen S., Nordhausen, K. and Oja, H. (2015), Fourth Moments and Independent Component Analysis, Statistical Science, 30, 372--390.

Miettinen, J., Nordhausen, K. and Taskinen, S. (2017), Blind Source Separation Based on Joint Diagonalization in R: The Packages JADE and BSSasymp, Journal of Statistical Software, 76, 1--31, <doi:10.18637/jss.v076.i02>.

See Also

ics

Examples

# 3 source and 3 signals

S <- cbind(rt(1000, 4), rnorm(1000), runif(1000))
A <- matrix(rnorm(9), ncol = 3)
X <- S %*% t(A)
res<-FOBI(X)
MD(coef(res),A)