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This webinar explores the extension of essential information-based data reduction to trilinear datasets through a novel algorithmic procedure based on Higher Order Singular Value Decomposition (HOSVD). Learn how this approach compresses multivariate measurements while preserving local rank properties, enabling faster and accurate data factorization. The 46-minute presentation evaluates the algorithm's performance in both real-world and simulated scenarios, demonstrating its benefits in multiway fluorescence spectroscopy and imaging applications. Presented by Raffaele Vitale from the LASIRE laboratory at Université de Lille, this research was conducted in collaboration with scientists from the University of Sistan and Baluchestan (Iran) and Radboud University (Netherlands). The webinar is part of the Chemometrics & Machine Learning in Copenhagen series.
