Sparse and spurious: dictionary learning with noise and outliers

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• Rémi Gribonval, Research Director with Inria (Rennes, France)
• Friday 10 July 2015, 11:00-12:00
• Dennis Gabor Seminar Room, 611, level 6, EEE Dept. .

If you have a question about this talk, please contact Pier Luigi Dragotti.

Abstract: Sparse modeling has become highly popular in signal processing and machine learning, where many tasks can be expressed as under-determined linear inverse problems. Together with a growing family of low-dimensional signal models, sparse models expressed with signal dictionaries have given rise to a rich set of algorithmic principles combining provably good performance with bounded complexity. In practice, from denoising to inpainting and super-resolution, applications require choosing a “good” dictionary. This key step can be empirically addressed through data-driven principles known as dictionary learning. In this talk I will draw a panorama of dictionary learning for low-dimensional modeling. After reviewing the basic empirical principles of dictionary learning and related matrix factorizations such as PCA , K-means and NMF , we will discuss techniques to learn dictionaries with controlled computational efficiency, as well as a series of recent theoretical results establishing the statistical significance of learned dictionaries even in the presence of noise and outliers.

Biography: Rémi Gribonval is a Research Director with Inria in Rennes, France, and the scientific leader of the PANAMA research group on sparse audio processing. A former student at Ecole Normale Supérieure, Paris, he received the Ph. D. degree in applied mathematics from the University of Paris-IX Dauphine in 1999. His research focuses on mathematical signal processing, machine learning, approximation theory and statistics, with an emphasis on low-dimensional modeling, dictionary learning and compressed sensing. In 2011, he was awarded the Blaise Pascal Award of the GAMNI -SMAI by the French Academy of Sciences, and a starting investigator grant from the European Research Council. He founded the series of international workshops SPARS on Signal Processing with Adaptive/Sparse Representations. He is a member of the IEEE Signal Processing Theory and Methods Technical Committee, and an IEEE fellow.

This talk is part of the Featured talks series.

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• Andrea Picciau's list
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• COMMSP Seminar
• Dennis Gabor Seminar Room, 611, level 6, EEE Dept.
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