Independent Component Analysis by Robust Distance Correlation

TL;DR

This paper introduces RICA, a robust independent component analysis method that uses distance correlation and a novel bowl transform to effectively identify independent sources even in the presence of outliers.

Contribution

The paper proposes RICA, a new robust ICA approach utilizing distance correlation and a bowl transform to improve outlier robustness and source separation accuracy.

Findings

RICA outperforms existing ICA methods in robustness to outliers.

RICA is strongly consistent with parametric convergence rates.

Application to the cocktail party problem demonstrates practical effectiveness.

Abstract

Independent component analysis (ICA) is a powerful tool for decomposing a multivariate signal or distribution into fully independent sources, not just uncorrelated ones. Unfortunately, most approaches to ICA are not robust against outliers. Here we propose a robust ICA method called RICA, which estimates the components by minimizing a robust measure of dependence between multivariate random variables. The dependence measure used is the distance correlation (dCor). In order to make it more robust we first apply a new transformation called the bowl transform, which is bounded, one-to-one, continuous, and maps far outliers to points close to the origin. This preserves the crucial property that a zero dCor implies independence. RICA estimates the independent sources sequentially, by looking for the component that has the smallest dCor with the remainder. RICA is strongly consistent and has the usual parametric rate of convergence. Its robustness is investigated by a simulation study, in which it generally outperforms its competitors. The method is illustrated on three applications, including the well-known cocktail party problem.