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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."

Field of science

  • /natural sciences/computer and information sciences/data science/big data
  • /engineering and technology/environmental engineering/remote sensing
  • /natural sciences/computer and information sciences/data science/data processing
  • /natural sciences/computer and information sciences/artificial intelligence/machine learning
  • /natural sciences/computer and information sciences/data science/data analysis
  • /natural sciences/physical sciences/astronomy/astrophysics/dark matter

Call for proposal

H2020-FETOPEN-2014-2015-RIA
See other projects for this call

Funding Scheme

RIA - Research and Innovation action

Coordinator

COMMISSARIAT A L ENERGIE ATOMIQUE ET AUX ENERGIES ALTERNATIVES
Address
Rue Leblanc 25
75015 Paris 15
France
Activity type
Research Organisations
EU contribution
€ 857 875

Participants (4)

IDRYMA TECHNOLOGIAS KAI EREVNAS
Greece
EU contribution
€ 560 000
Address
N Plastira Str 100
70013 Irakleio
Activity type
Research Organisations
SAFRAN ELECTRONICS & DEFENSE
France
EU contribution
€ 288 125
Address
72-76 Rue Henry Farman
75015 Paris
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
UNIVERSITY COLLEGE LONDON
United Kingdom
EU contribution
€ 485 397,50
Address
Gower Street
WC1E 6BT London
Activity type
Higher or Secondary Education Establishments
TECHNISCHE UNIVERSITAT BERLIN
Germany
EU contribution
€ 511 000
Address
Strasse Des 17 Juni 135
10623 Berlin
Activity type
Higher or Secondary Education Establishments