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Last modified : 4/2/09

AIMS OF THE WORKSHOP

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 is to present and discuss novel ideas, works and results, both experimental and theoretical, related to this rapidly evolving area of research.

FOCUS OF THE WORKSHOP

Contributions are expected on the following topics (non-limitative list):

• Sparse coding, vector quantization and dictionary learning
• Sparse approximation algorithms : performance and complexity analysis, new methodologies, ...
• Compressed sensing
• Simultaneous processing of multiple signals/images
• Sparse/structured signal representations, visualization
• Compression and coding
• Feature extraction, classification, detection
• Source separation
• Sparsity measures in approximation theory, information theory and statistics
• Applications to image, audio, video, medical, multimedia and multichannel data processing
• Other