The FOBI method for independent component analysis (ICA). We assume that all components have different kurtosis values.
Usage
FOBI(X, na.action = na.fail)
Arguments
X
a numeric matrix.
na.action
A function which indicates what should happen when the data
contain 'NA's. Default is to 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.
Note
More general is the function ics in the ICS package.
Author(s)
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
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)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(JADE)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/JADE/FOBI.Rd_%03d_medium.png", width=480, height=480)
> ### Name: FOBI
> ### Title: Function to perform FOBI for ICA
> ### Aliases: FOBI
> ### Keywords: multivariate
>
> ### ** 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)
[1] 0.3674742
>
>
>
>
>
> dev.off()
null device
1
>