Robust Sparse Blind Source Separation

TL;DR

This paper introduces rGMCA, a robust algorithm for blind source separation that effectively handles outliers by jointly estimating sources, the mixing matrix, and outliers, improving accuracy in real-world noisy data.

Contribution

The paper proposes a novel robust BSS algorithm, rGMCA, which explicitly estimates outliers and enhances separation accuracy compared to standard methods.

Findings

rGMCA outperforms standard BSS techniques in estimating the mixing matrix.

The algorithm effectively estimates sources and outliers simultaneously.

Numerical experiments validate the robustness and efficiency of rGMCA.

Abstract

Blind Source Separation is a widely used technique to analyze multichannel data. In many real-world applications, its results can be significantly hampered by the presence of unknown outliers. In this paper, a novel algorithm coined rGMCA (robust Generalized Morphological Component Analysis) is introduced to retrieve sparse sources in the presence of outliers. It explicitly estimates the sources, the mixing matrix, and the outliers. It also takes advantage of the estimation of the outliers to further implement a weighting scheme, which provides a highly robust separation procedure. Numerical experiments demonstrate the efficiency of rGMCA to estimate the mixing matrix in comparison with standard BSS techniques.