# Heuristics for Efficient Sparse Blind Source Separation

## TL;DR

This paper introduces a heuristic-enhanced optimization strategy for sparse blind source separation, improving practical results especially in astrophysical data analysis, by addressing limitations of existing algorithms like PALM.

## Contribution

It proposes a new heuristic approach combined with PALM to enhance the practical performance of sparse BSS algorithms, particularly for complex astrophysical data.

## Key findings

- Improved separation results on astrophysical data
- Heuristic approach enhances PALM performance
- Demonstrated relevance in realistic data scenarios

## Abstract

Sparse Blind Source Separation (sparse BSS) is a key method to analyze multichannel data in fields ranging from medical imaging to astrophysics. However, since it relies on seeking the solution of a non-convex penalized matrix factorization problem, its performances largely depend on the optimization strategy. In this context, Proximal Alternating Linearized Minimization (PALM) has become a standard algorithm which, despite its theoretical grounding, generally provides poor practical separation results. In this work, we propose a novel strategy that combines a heuristic approach with PALM. We show its relevance on realistic astrophysical data.