Iterative optimization of convex divergence: applications to independent component analysis
Yasuo Matsuyama · 2003
Iterative optimization of convex diver- gence is discussed. The convex divergence is used as a measure of independence for ICA algorithms. An ad- ditional method to incorporate supervisory informa- tion to reduce the ICA's permutation indeterminacy is also given. Speed of the algorithm is examined us- ing a set of simulated data and brain fMRI data.