  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.
