Manifold Optimization in Data Science

13.05.2026, 12:00  –  Campus Golm, Building 9, Room 1.22
Forschungsseminar Numerische Analysis

Max Pfeffer (Universität Potsdam)

Matrix and tensor factorizations are widely applied in Data Science for dimensionality and noise reduction as well as for feature extraction. Often, additional constraints are imposed on the factors in order to improve uniqueness and interpretability of the results. We consider several specific factorization formats with smooth and nonsmooth constraints that can be computed using techniques from Riemannian optimization. For this, existing methods need to be adapted according to the problem at hand. Furthermore, we apply our methods also for Data Fusion, where several data sets are factorized simultaneously.