Raffaele Vitale
Univ. Lille, CNRS, LASIRE (UMR 8516), Laboratoire Avancé de Spectroscopie pour les Interactions, la Réactivité et l’Environnement, F-59000 Lille, France
In the domain of bilinear curve resolution, the identification and extraction of essential information from sets of multivariate measurements has recently garnered significant attention primarily in the light of the fact that such an approach is capable of compressing the data at hand while preserving their local rank properties and, thus, enabling their accurate factorisation in a dramatically shorter amount of time. In this presentation, the idea of essential information-based data reduction is extended to trilinear datasets through the description of an original algorithmic procedure leveraging the principles of Higher Order Singular Value Decomposition (HOSVD). The performance of this novel algorithm will be evaluated in both real-world and simulated scenarios which will permit to highlight the benefits it can bring in domains like multiway fluorescence spectroscopy and imaging.
This work was conducted in collaboration with Azar Azizi (University of Sistan and Baluchestan, Zahedan, Iran), Nematollah Omidikia (University of Sistan and Baluchestan, Zahedan, Iran), Mahdiyeh Ghaffari (Radboud University, Nijmegen, The Netherlands), and Cyril Ruckebusch (Université de Lille, Lille, France).
Join the webinar March 3, 15.00 (CET) 2025. See more at https://www.linkedin.com/events/three-waydatareductionbasedones7297979296994750464/comments/
The recorded webinar is now available at https://youtu.be/MwQYStYjwnM and the slides can be found here.