摘要
This study proposes a non-parametric ICA method, called ECOPICA, which describes the joint distribution of data by empirical copulas and measures the dependence between recovery signals by an independent test statistic. We employ the grasshopper algorithm to optimize the proposed objective function. Several acceleration tricks are further designed to enhance the computational efficiency of the proposed algorithm under the parallel computing framework. Our simulation and empirical analysis show that ECOPICA produces better and more robust recovery performances than other well-known ICA approaches for various source distribution shapes, especially when the source distribution is skewed or near-Gaussian.