Nonparametric Bayesian Sparse Graph Linear Dynamical Systems

Rahi Kalantari; Joydeep Ghosh; Mingyuan Zhou

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arXiv · 1802.07434

Nonparametric Bayesian Sparse Graph Linear Dynamical Systems

Rahi Kalantari·Joydeep Ghosh·Mingyuan Zhou

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Abstract

A nonparametric Bayesian sparse graph linear dynamical system (SGLDS) is proposed to model sequentially observed multivariate data. SGLDS uses the Bernoulli-Poisson link together with a gamma process to generate an infinite dimensional sparse random graph to model state transitions. Depending on the sparsity pattern of the corresponding row and column of the graph affinity matrix, a latent state of SGLDS can be categorized as either a non-dynamic state or a dynamic one. A normal-gamma construction is used to shrink the energy captured by the non-dynamic states, while the dynamic states can be further categorized into live, absorbing, or noise-injection states, which capture different types of dynamical components of the underlying time series. The state-of-the-art performance of SGLDS is demonstrated with experiments on both synthetic and real data.

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BibTeX RIS

Rahi Kalantari, Joydeep Ghosh, Mingyuan Zhou. 2018-02-21. Nonparametric Bayesian Sparse Graph Linear Dynamical Systems. https://arxiv.org/abs/1802.07434

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