DEDALE

H2020-FET-OPEN-RIA-2014-2015,  FET-Open research projects

Data Learning on Manifolds and Future Challenges

 Welcome to DEDALE

 

New era of big data, with massive, complex and heterogeneous data. Novel data analysis methods in machine learning allows us  a better preservation of theintrinsic physical properties of real data that generally live on intricate spaces, such as signal manifolds.

The DEDALE interdisciplinary project intends to develop the next generation of data analysis methods for such data set in order to probe the fine structure andextract information in high dimensional data sets, in astrophysics and remote sensing.

Our project have three main scientific directions:

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,

iii) Show the reliability of the proposed methods in two different applications: one in cosmology and one in remote sensing.

Project web site HERE