High-Dimensional Data Processing: From Theory to Imaging Application (HiDDaProTImA)

Objectives

The unstoppable increase in the volume of data stored, transmitted and interpreted by fixed and mobile devices strongly calls for the study of efficient solutions in processing the information contained in high-dimensional signals. Such need has been reflected in the recent flourishing of research efforts from the statistics, machine learning, computer science and signal processing communities. 

The research work carried out within the project HiDDaProTImA has the objective of studying fundamental limits on how information can be extracted and processed from high-dimensional data, and to design methods and algorithms able to achieve such fundamental limits. On leveraging probabilistic and deterministic mathematical frameworks to model high-dimensional data, we aim at providing answers to the following research questions:

  1. What is the minimum number of features that we need to extract from high-dimensional data in order to reliably extract information? And how to extract such information?
  2. What is the advantage represented by the presence of multi-modal additional information in processing high dimensional data? And how should we optimally capture such side information?
  3. What is the optimal way to learn dictionaries to represent? What is the interplay between dictionary learning and optimizing feature extraction?

Although the answers to such research questions have an important impact on different application fields involving information processing of high-dimensional data, the main focus of the work carried out within the project HiDDAProTImA has been that of imaging applications.

Project outcomes

Acknowledgments

The project HiDDaProTImA is funded by the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 655282.

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