ECOPICA: empirical copula-based independent component analysis

Hung Kai Pi, Mei Hui Guo, Ray Bing Chen, Shih Feng Huang

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number52
JournalStatistics and Computing
Issue number1
StatePublished - Feb 2024


  • Blind image separation
  • Cocktail-party problem
  • Copula
  • Grasshopper optimization algorithm
  • Independent component analysis


Dive into the research topics of 'ECOPICA: empirical copula-based independent component analysis'. Together they form a unique fingerprint.

Cite this