Joint Problems in Learning Multiple Dynamical Systems
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Abstract (arXiv)
Clustering of time series is a well-studied problem, with applications ranging from quantitative, personalized models of metabolism obtained from metabolite concentrations to state discrimination in quantum information theory. We consider a variant, where given a set of trajectories and a number of parts, we jointly partition the set of trajectories and learn linear dynamical system (LDS) models for each part, so as to minimize the maximum error across all the models. We present globally convergent methods and EM heuristics, accompanied by promising computational results. The key highlight of this method is that it does not require a predefined hidden state dimension but instead provides an upper bound. Additionally, it offers guidance for determining regularization in the system identification.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 23 of its 70 references that are in the map. First posted on arXiv 3 Nov 2023.
Builds on
23 of 70 references.
References · 23
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