Copula Component Analysis

TL;DR

This paper introduces Copula Component Analysis (CCA), a novel framework for blind source separation that models dependencies among sources using copulas, extending ICA to handle dependent components for more accurate estimation.

Contribution

The paper proposes CCA as a generalization of ICA that incorporates dependency structures via copulas, enabling better source separation when independence assumptions fail.

Findings

CCA effectively models dependent sources

Improves source separation accuracy over traditional ICA

Two-phase inference enhances estimation performance

Abstract

A framework named Copula Component Analysis (CCA) for blind source separation is proposed as a generalization of Independent Component Analysis (ICA). It differs from ICA which assumes independence of sources that the underlying components may be dependent with certain structure which is represented by Copula. By incorporating dependency structure, much accurate estimation can be made in principle in the case that the assumption of independence is invalidated. A two phrase inference method is introduced for CCA which is based on the notion of multidimensional ICA.