Stochastic Trapping in a Solvable Model of On-Line Independent Component Analysis
Stochastic Trapping in a Solvable Model of On-Line Independent Component Analysis
Previous analytical studies of on-line independent component analysis (ICA) learning rules have focused on asymptotic stability and efficiency. In practice, the transient stages of learning are often more significant in determining the success of an algorithm. This is demonstrated here with an analysis of a Hebbian ICA algorithm, which can …