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Content archived on 2024-05-29

Utilising and enhancing unsupervised feature extraction

Objective

All data-driven research areas depend eminently on intelligent data analysis. This project will advance the state of the art in unsupervised data analysis by addressing two important issues:
- For practitioners it is very difficult to decide which of the numerous methods for unsupervised feature extraction is appropriate for given data. This project will develop practical tools for model selection to utilise the plenty of approaches.
- New paradigms in feature extraction lead to special features with special properties.

To this end we will focus on developing methods that extract statistically independent features, hereby aiming to solve the problem of nonlinear ICA. Nonlinear independent features give a particularly succinct description of data. Methods for nonlinear ICA that can deal with strongly nonlinearly mixed data might become the basis for new sophisticated approaches to density estimation in high dimensions.

Fields of science (EuroSciVoc)

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Keywords

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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.

Call for proposal

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FP6-2004-MOBILITY-5
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.

EIF - Marie Curie actions-Intra-European Fellowships

Coordinator

THE UNIVERSITY OF EDINBURGH
EU contribution
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Total cost

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