FastICA contrast functions.

Description

contrast functions for computing the negentropy criteria used in FastICA; see references.

Usage

G1(s, a=1)
G0(s, a=1)

Arguments

Value

a list with components

Author(s)

Trevor Hastie and Rob Tibshirani

References

Hyvarinen, A., Karhunen, J. and Oja, E. (2001). Independent Component Analysis, Wiley, New York
Hastie, T. and Tibshirani, R. (2003) Independent Component Analysis through Product Density Estimation in Advances in Neural Information Processing Systems 15 (Becker, S. and Obermayer, K., eds), MIT Press, Cambridge, MA. pp 649-656
Hastie, T., Tibshirani, R. and Friedman, J. (2009) Elements of Statistical Learning (2nd edition), Springer.
http://www-stat.stanford.edu/~hastie/Papers/ESLII.pdf

See Also

GPois and ProDenICA

Examples

p=2
### Can use letters a-r below for dist
dist="n" 
N=1024
A0<-mixmat(p)
s<-scale(cbind(rjordan(dist,N),rjordan(dist,N)))
x <- s %*% A0
fit=ProDenICA(x,Gfunc=G1, whiten=TRUE)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(ProDenICA)
Loading required package: gam
Loading required package: splines
Loading required package: foreach
Loaded gam 1.12

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/ProDenICA/G1.Rd_%03d_medium.png", width=480, height=480)
> ### Name: G1
> ### Title: FastICA contrast functions.
> ### Aliases: G1 G0
> ### Keywords: distribution
> 
> ### ** Examples
> p=2
> ### Can use letters a-r below for dist
> dist="n" 
> N=1024
> A0<-mixmat(p)
> s<-scale(cbind(rjordan(dist,N),rjordan(dist,N)))
> x <- s %*% A0
> fit=ProDenICA(x,Gfunc=G1, whiten=TRUE)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>