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Data Learning on Manifolds and Future Challenges

Data Learning on Manifolds and Future Challenges

Objective

"Future data processing challenges in science will enter the ""Big Data"" era, involving massive, as well as complex and heterogeneous data. Extracting, with high precision, every bit of information from scientific data requires overcoming fundamental statistical challenges, which mandate the design of dedicated methods that must be both effective enough to capture the intricacy of real-world datasets and robust to the high complexity of instrumental measurements. Moreover, future datasets, such as those provided by the space mission Euclid, will involve at least gigascale data, which will make mandatory the development of new, physically relevant, data models and the implementation of effective and computationally efficient processing tools. The recent emergence of novel data analysis methods in machine learning should foster a new modeling framework, allowing for a better preservation of the intrinsic physical properties of real data that generally live on intricate spaces, such as signal manifolds. Furthermore, advances in operations research and optimization theory pave the way for effective solutions to overcome the large-scale data processing bottlenecks. In this context, the objective of the DEDALE project is threefold: i) introduce new models and methods to analyze and restore complex, multivariate, manifold-based signals; ii) exploit the current knowledge in optimization and operations research to build efficient numerical data processing algorithms in the large-scale settings; and iii) show the reliability of the proposed data modeling and analysis technologies to tackle Scientific Big Data challenges in two different applications: one in cosmology, to map the dark matter mass map of the universe, and one in remote sensing to increase the capabilities of automatic airborne imaging analysis systems."

Coordinator

COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES

Address

Rue Leblanc 25
75015 Paris 15

France

Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 857 875

Participants (4)

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FOUNDATION FOR RESEARCH AND TECHNOLOGY HELLAS

Greece

EU Contribution

€ 560 000

SAFRAN ELECTRONICS & DEFENSE

France

EU Contribution

€ 288 125

UNIVERSITY COLLEGE LONDON

United Kingdom

EU Contribution

€ 485 397,50

TECHNISCHE UNIVERSITAT BERLIN

Germany

EU Contribution

€ 511 000

Project information

Grant agreement ID: 665044

  • Start date

    1 October 2015

  • End date

    30 September 2018

Funded under:

H2020-EU.1.2.1.

  • Overall budget:

    € 2 702 397,50

  • EU contribution

    € 2 702 397,50

Coordinated by:

COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES

France