I have uploaded a new package to CRAN called ProDenICA.
This fits ICA models directly via product-density estimation
of the source densities. This package was promised on page 567 in the
2nd edition of our book 'Elements of Statistical Learning'
(Hastie, Tibshirani and Friedman, 2009, Springer) . Apologies that it is so
late.
The method fits each source density by a tilted gaussian density, where
the log of the tilting function is modeled by a smoothing spline. This
function is then used as a contrast function for computing the negentropy
measure for this source component. The estimation is achieved by fitting a
poisson
GAM model for each component, with the log-gaussian as an offset.
The method was first described in
Hastie, T. and Tibshirani, R. (2003). Independent component analysis
through product density estimation, in S. T. S. Becker and K. Obermayer
(eds), Advances in Neural Information Processing Systems 15,
MIT Press, Cambridge, MA, pp. 649-656.
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Trevor Hastie hastie at stanford.edu
Professor, Department of Statistics, Stanford University
Phone: (650) 725-2231 (Statistics) Fax: (650) 725-8977
(650) 498-5233 (Biostatistics) Fax: (650) 725-6951
URL: http://www-stat.stanford.edu/~hastie
address: room 104, Department of Statistics, Sequoia Hall
390 Serra Mall, Stanford University, CA 94305-4065
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