SMALL

Sparse models, algorithms, and learning for large scale data

Coordinatore	INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE    more  Organization address address: DOMAINE DE VOLUCEAU, ROCQUENCOURT N/A city: LE CHESNAY CEDEX postcode: 78153 contact info Cognome: GUILLOIS, JEAN-PAUL Email: send email Telefono: -99847217 Fax: -99847140
Nazionalità Coordinatore	France [FR]
Sito del progetto	http://www.small-project.eu
Totale costo	2˙523˙139 €
EC contributo	1˙919˙167 €
Programma	FP7-ICT Specific Programme "Cooperation": Information and communication technologies
Funding Scheme	CP
Anno di inizio	2009
Periodo (anno-mese-giorno)	2009-02-01   -   2012-01-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET EN AUTOMATIQUE  Organization address address: DOMAINE DE VOLUCEAU, ROCQUENCOURT N/A city: LE CHESNAY CEDEX postcode: 78153 contact info Cognome: GUILLOIS, JEAN-PAUL Email: send email Telefono: -99847217 Fax: -99847140	FR (LE CHESNAY CEDEX)	coordinator	0.00
2	ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE	CH	participant	0.00
3	QUEEN MARY AND WESTFIELD COLLEGE, UNIVERSITY OF LONDON  Organization address address: MILE END ROAD city: LONDON postcode: E1 4NS contact info Cognome: N/A	UK (LONDON)	participant	0.00
4	TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY.  Organization address address: Technion City-Senate Building city: HAIFA postcode: 32000 contact info Cognome: N/A	IL (HAIFA)	participant	0.00
5	THE UNIVERSITY OF EDINBURGH  Organization address address: Old College, South Bridge city: EDINBURGH contact info Cognome: N/A	UK (EDINBURGH)	participant	0.00

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 Obiettivo del progetto (Objective)

SMALL will develop a new foundational framework for processing signals, using adaptive sparse structured representations. A key discriminating feature of sparse representations, which opened up the horizons to new ways of thinking in signal processing including compressed sensing, has been the focus on developing reliable algorithms with provable performance and bounded complexity. Yet, such approaches are simply inapplicable in many scenarios for which no suitable sparse model is known. Moreover, the success of sparse models heavily depends on the choice of a 'dictionary' to reflect the natural structures of a class of data, but choosing a dictionary is currently something of an 'art', using expert knowledge rather than automatically applicable principles. Inferring a dictionary from training data is key to the extension of sparse models for new exotic types of data.

SMALL will explore new generations of provably good methods to obtain inherently data-driven sparse models, able to cope with large-scale and complicated data much beyond state-of-the-art sparse signal modelling. The project will develop a foundational theoretical framework for the dictionary-learning problem, and scalable algorithms for the training of structured dictionaries. SMALL algorithms will be evaluated against state-of-the art alternatives and we will demonstrate our approach on a range of showcase applications. We will organise two open workshops to disseminate our results and get feedback from the research community. The proposed framework will deeply impact the research landscape since the new models, approaches and algorithms will be generically applicable to a wide variety of signal processing problems, including acquisition, enhancement, manipulation, interpretation and coding. This new line of attack will lead to many new theoretical and practical challenges, with a potential to reshape both the signal processing research community and the burgeoning compressed sensing industry.