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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural sciences computer and information sciences data science
- natural sciences mathematics pure mathematics algebra linear algebra
- natural sciences computer and information sciences artificial intelligence computer vision
- natural sciences computer and information sciences artificial intelligence machine learning
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
ERC-2012-StG_20111012
See other projects for this call
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Host institution
66123 Saarbrucken
Germany
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.