IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
A non-parametric hierarchical model to discover behavior dynamics from tracks
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Stochastic variational inference
The Journal of Machine Learning Research
Autonomous tactile perception: A combined improved sensing and Bayesian nonparametric approach
Robotics and Autonomous Systems
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Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. Our Bayesian nonparametric approach utilizes a hierarchical Dirichlet process prior to learn an unknown number of persistent, smooth dynamical modes. We additionally employ automatic relevance determination to infer a sparse set of dynamic dependencies allowing us to learn SLDS with varying state dimension or switching VAR processes with varying autoregressive order. We develop a sampling algorithm that combines a truncated approximation to the Dirichlet process with efficient joint sampling of the mode and state sequences. The utility and flexibility of our model are demonstrated on synthetic data, sequences of dancing honey bees, the IBOVESPA stock index and a maneuvering target tracking application.