Joint Problems in Learning Multiple Dynamical Systems

arXiv:2311.02181v2 Announce Type: replace-cross
Abstract: 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 …

abstract applications arxiv clustering cs.ai cs.lg discrimination information math.oc multiple personalized quantitative quantum series set state systems theory time series type