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Utilising and enhancing unsupervised feature extraction

Objectif

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.

Champ scientifique (EuroSciVoc)

Mots‑clés

Thème(s)

Appel à propositions

FP6-2004-MOBILITY-5
Voir d’autres projets de cet appel

Régime de financement

EIF - Marie Curie actions-Intra-European Fellowships

Coordinateur

THE UNIVERSITY OF EDINBURGH
Contribution de l’UE
Aucune donnée
Coût total
Aucune donnée