Learning Latent Variable and Predictive Models of Dynamical Systems

摘要

In this thesis we propose novel learning algorithms that address the issues of model selection, local minima and instability in learning latent variable models. We show that certain 'predictive' latent variable model learning methods bridge the gap between latent variable and predictive models. We also propose a novel latent variable model, the Reduced-Rank HMM (RR-HMM), that combines desirable properties of discrete and real-valued latent-variable models. We show that reparameterizing the class of RR-HMMs yields a subset of PSRs, and propose an asymptotically unbiased predictive learning algorithm for RR-HMMs and PSRs along with finite-sample error bounds for the RR-HMM case. In terms of efficiency and accuracy, our methods outperform alternatives on dynamic texture videos, mobile robot visual sensing data, and other domains.