ProDenICA (version 1.1)

rjordan: Generate source densities for ICA

Description

Functions for generating the source densities used in Bach and Jordan (2002), and reused in Hastie and Tibshirani (2003)

Usage

rjordan(letter, n, ...)
djordan(letter, x, ...)

Arguments

letter

one of the 18 letters a-r; see Figure 14.42 on page 569 of 'Elements of Statistical Learning'

n

number of samples

x

ordinates at which to compute density

…

place filler for additional arguments

Value

Either a vector of density values the length of x for djordan, or a vector of n draws for rjordan

Details

This function produces the example densities used in Bach and Jordan (2002), and copied by Hastie and Tibshirani (2003). They include the 't', uniform, mixtures of exponentials and many mixtures of gaussian densities. Each are standardized to have mean zero and variance 1.

References

Bach, F. and Jordan, M. (2002). Kernel independent component analysis, Journal of Machine Learning Research 3: 1-48 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. https://hastie.su.domains/ElemStatLearn/printings/ESLII_print12_toc.pdf

See Also

ProDenICA

Examples

# NOT RUN {
dist="n" 
N=1024
s<-scale(cbind(rjordan(dist,N),rjordan(dist,N)))
# }