Learning Mixtures of Linear Dynamical Systems. (arXiv:2201.11211v1 [stat.ML])

Web: http://arxiv.org/abs/2201.11211

We study the problem of learning a mixture of multiple linear dynamical
systems (LDSs) from unlabeled short sample trajectories, each generated by one
of the LDS models. Despite the wide applicability of mixture models for
time-series data, learning algorithms that come with end-to-end performance
guarantees are largely absent from existing literature. There are multiple
sources of technical challenges, including but not limited to (1) the presence
of latent variables (i.e. the unknown labels of trajectories); (2) the
possibility that the …

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