A mean-field theory for multi-component online ICA in high dimensions predicts decoupled and competition phases, explicit learnability boundaries, and a staircase effect in the number of recoverable components as a function of learning rate.
Independent component analysis: algorithms and applications.Neural networks, 13(4-5):411–430
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Learnability and Competition in High-Dimensional Multi-Component ICA
A mean-field theory for multi-component online ICA in high dimensions predicts decoupled and competition phases, explicit learnability boundaries, and a staircase effect in the number of recoverable components as a function of learning rate.