The cookie-related information is fully under our control. These cookies are not used for any purpose other than those described here. Unibo policy
Graph methods for machine learning have been found to be extraordinarily successful in several imaging and data analysis applications. They aim to build a graph from the data by encoding similarities between elements and use possible non-local similarity measures for comparison, clustering and classification. The study of large-data limits of state-of-the-art graph models such as Ginzburg-Landau functionals, Cheeger cuts etc. is fundamental for the design of efficient optimisation strategies. In this mini-symposium we gather experts in the field of mathematical graph modelling and large-data convergence to highlight analogies and differences between continuum and discrete variational models for data analysis.