SLRA

"Structured low-rank approximation: Theory, algorithms, and applications"

Coordinatore	VRIJE UNIVERSITEIT BRUSSEL    more Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie.
Nazionalità Coordinatore	Belgium [BE]
Totale costo	782˙960 €
EC contributo	782˙960 €
Programma	FP7-IDEAS-ERC Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	ERC-2010-StG_20091028
Funding Scheme	ERC-SG
Anno di inizio	2011
Periodo (anno-mese-giorno)	2011-01-01   -   2015-12-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	UNIVERSITY OF SOUTHAMPTON  Organization address address: Highfield city: SOUTHAMPTON postcode: SO17 1BJ contact info Titolo: Mr. Nome: David Cognome: Woolley Email: send email Telefono: +44 23 8059 8341	UK (SOUTHAMPTON)	beneficiary	219˙108.00
2	VRIJE UNIVERSITEIT BRUSSEL  Organization address address: PLEINLAAN 2 city: BRUSSEL postcode: 1050 contact info Titolo: Dr. Nome: Ivan Valentinov Cognome: Markovsky Email: send email Telefono: +32 2 6292947 Fax: +32 2 6292850	BE (BRUSSEL)	hostInstitution	563˙852.00
3	VRIJE UNIVERSITEIT BRUSSEL  Organization address address: PLEINLAAN 2 city: BRUSSEL postcode: 1050 contact info Titolo: Mr. Nome: Nik Cognome: Claesen Email: send email Telefono: +32 2 629 2210 Fax: +32 2 6293640	BE (BRUSSEL)	hostInstitution	563˙852.00

Mappa

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 Obiettivo del progetto (Objective)

'Today's state-of-the-art methods for data processing are model based. We propose a fundamentally new approach that does not depend on an explicit model representation and can be used for model-free data processing. From a theoretical point of view, the prime advantage of the newly proposed paradigm is conceptual unification of existing methods. From a practical point of view, the proposed paradigm opens new possibilities for development of computational methods for data processing.

The underlying computational tool in the proposed setting is low-rank approximation. Recent work by the applicant, co-workers, and others has demonstrated advantages of computational methods based on low-rank approximation over classical methods, based on solution of linear systems of equations. In this proposal, we will further advance the theory and algorithms for low-rank approximation by developing robust and efficient local optimisation methods and methods based on convex relaxations.

Low-rank approximation has applications in systems and control, signal processing, computer algebra, and machine learning, to name a few. Generic examples in system theory and signal processing are model reduction and system identification. Dimensionality reduction, classification, and information retrieval problems in machine learning can be formulated and solved as low-rank approximation problems, thus benefiting from the theory, algorithms, and numerical software tools developed in this research proposal. Beyond the scope of the proposal, we envisage that the newly proposed paradigm will catalyse cross-disciplinary research, leading to selection of the best theoretical tools and computational methods available as well as development of new ones by a synergy of ideas from different application domains.'