NOLEPRO
Nonlinear Eigenproblems for Data Analysis
| Coordinatore | UNIVERSITAET DES SAARLANDES
Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie. |
| Nazionalità Coordinatore | Germany [DE] |
| Totale costo | 1˙271˙992 € |
| EC contributo | 1˙271˙992 € |
| 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-2012-StG_20111012 |
| Funding Scheme | ERC-SG |
| Anno di inizio | 2012 |
| Periodo (anno-mese-giorno) | 2012-10-01 - 2017-09-30 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
UNIVERSITAET DES SAARLANDES
Organization address
address: CAMPUS contact info |
DE (SAARBRUECKEN) | hostInstitution | 1˙271˙992.00 |
| 2 |
UNIVERSITAET DES SAARLANDES
Organization address
address: CAMPUS contact info |
DE (SAARBRUECKEN) | hostInstitution | 1˙271˙992.00 |
Mappa
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Obiettivo del progetto (Objective)
'In machine learning and exploratory data analysis, the major goal is the development of solutions for the automatic and efficient extraction of knowledge from data. This ability is key for further progress in science and engineering. A large class of data analysis methods is based on linear eigenproblems. While linear eigenproblems are well studied, and a large part of numerical linear algebra is dedicated to the efficient calculation of eigenvectors of all kinds of structured matrices, they are limited in their modeling capabilities. Important properties like robustness against outliers and sparsity of the eigenvectors are impossible to realize. In turn, we have shown recently that many problems in data analysis can be naturally formulated as nonlinear eigenproblems.
In order to use the rich structure of nonlinear eigenproblems with an ease similar to that of linear eigenproblems, a major goal of this proposal is to develop a general framework for the computation of nonlinear eigenvectors. Furthermore, the great potential of nonlinear eigenproblems will be explored in various application areas. As the scope of nonlinear eigenproblems goes far beyond data analysis, this project will have major impact not only in machine learning and its use in computer vision, bioinformatics, and information retrieval, but also in other areas of the natural sciences.'