About:

Over the last five years, theoretical advances in sparse representations have highlighted their potential to impact all fundamental areas of signal processing, from blind source separation to feature extraction and classification, denoising, and detection. In particular, these techniques are at the core of compressed sensing, an emerging approach which proposes a radically new viewpoint on signal acquisition compared to Shannon sampling. There are also strong connections between sparse signal models and kernel methods, which algorithmic success on large datasets relies deeply on sparsity.

The purpose of the workshop was to present and discuss novel ideas, works and results, both experimental and theoretical, related to this rapidly evolving area of research.