Independent Component Analysis by Simultaneous Third- and Fourth-Order Cumulant DiagonalizationThese files are Matlab-files working with Matlab 5 and higher.
- sir.m: a function to evaluate the unmixing performance
- kennedy.mat: demo source signal needed by cubica_demo
All algorithms are explained in:
Blaschke, T. and Wiskott, L. (2002).
An Improved Cumulant Based Method for Independent Component Analysis.
Proc. Int. Conf. on Artificial Neural Networks, ICANN'02, pp. 1087-1093.
(abstract, Abstract: An improved method for independent component analysis based on the diagonalization of cumulant tensors is proposed. It is based on Comon's algorithm [Comon, 1994] but it takes third- and fourth-order cumulant tensors into account simultaneously. The underlying contrast function is also mathematically much simpler and has a more intuitive interpretation. It is therefore easier to optimize and approximate. A comparison with Comon's algorithm, JADE [Cardoso, 1993] and FastICA [Hyvärinen, 1999] on different data sets demonstrates its performance. bibtex, .pdf, .ps.gz)