The Scaling Limit of High-Dimensional Online Independent Component Analysis

NeurIPS 2017 Chuang WangYue M. Lu

We analyze the dynamics of an online algorithm for independent component analysis in the high-dimensional scaling limit. As the ambient dimension tends to infinity, and with proper time scaling, we show that the time-varying joint empirical measure of the target feature vector and the estimates provided by the algorithm will converge weakly to a deterministic measured-valued process that can be characterized as the unique solution of a nonlinear PDE... (read more)

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