Independent Component Analysis by Robust Distance Correlation
This addresses the issue of outlier sensitivity in ICA for signal processing and data analysis, representing an incremental improvement with a new robust variant.
The authors tackled the problem of non-robustness in independent component analysis (ICA) by proposing RICA, a method that uses a robust distance correlation measure to estimate independent sources, which outperformed competitors in simulations and was applied to problems like the cocktail party scenario.
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.