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Manifold Optimization in Data Science

Vortragsankündigung

Im Rahmen des Oberseminars Analysis/Numerik spricht

Herr Prof. Dr. Max Pfeffer (Universität Göttingen)

 

über

 

Title: Manifold Optimization in Data Science

 

Abstract: 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.

 

Der Vortrag findet statt am

 

Donnerstag, den 21.11.2024 um 14.15 Uhr im Raum W01 0-006

 

Interessierte sind herzlich eingeladen. 
 

21.11.2024 14:15 – Open End

Manifold Optimization in Data Science

Vortragsankündigung

Im Rahmen des Oberseminars Analysis/Numerik spricht

Herr Prof. Dr. Max Pfeffer (Universität Göttingen)

 

über

 

Title: Manifold Optimization in Data Science

 

Abstract: 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.

 

Der Vortrag findet statt am

 

Donnerstag, den 21.11.2024 um 14.15 Uhr im Raum W01 0-006

 

Interessierte sind herzlich eingeladen. 
 

21.11.2024 14:15 – Open End

(Changed: 20 Jun 2024)  Kurz-URL:Shortlink: https://uol.de/p87192c123597en
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