Mathematical Methods to Extract Information from High-dimensional Data (E01)

• Subject Area: Mathematics
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 416228727

With the aim to develop a general mathematical methodology for identifying events in high dimensional time series, the work program combines three different approximation methods. These are based on an analytic numerical approach for function approximation and dimension reduction, a stochastic approach for statistical clustering and a discrete graph-based method for generating a finite state transition graph model. In bringing these techniques together, the goal is to come up with new algorithmic methods that answer both theoretical and practical questions and support human-interpretable time series analysis.

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1410: Hybrid Societies: Humans Interacting with Embodied Technologies
• Applicant Institution: Technische Universität Chemnitz
• Project Heads: Professor Dr. Christoph Helmberg, until 12/2023; Professor Alois Pichler, Ph.D.; Professor Dr. Daniel Potts