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.
An application of the principle of maximum information preservation to linear systems
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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.