Signal Processing with Adaptive Sparse Structured Representations

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Signal Processing with Adaptive Sparse Structured Representations

 27 - 30 Jun 2011

Royal College of Physicians, Edinburgh

Scientific Organisers

  • Coralia Cartis, University of Oxford
  • Mike Davies, University of Edinburgh
  • Jared Tanner, University of Oxford

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.

Speakers

Francis Bach, Laboratoire d'Informatique de l'ENS

David J Brady, Duke University

David L Donoho, Stanford University

Remi Gribonval, Centre de Recherche INRIA Rennes

Yi Ma, University of Illinois

Joel Tropp, California Institute of Technology

Martin Vetterli, École Polytechnique Fédérale de Lausanne

Stephen J Wright, University of Wisconsin