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Variational Gaussian Process Dynamical Systems
Andreas Damianou · Michalis Titsias · Neil D Lawrence

Tue Dec 13 08:45 AM -- 02:59 PM (PST) @

High dimensional time series are endemic in applications of machine learning such as robotics (sensor data), computational biology (gene expression data), vision (video sequences) and graphics (motion capture data). Practical nonlinear probabilistic approaches to this data are required. In this paper we introduce the variational Gaussian process dynamical system. Our work builds on recent variational approximations for Gaussian process latent variable models to allow for nonlinear dimensionality reduction simultaneously with learning a dynamical prior in the latent space. The approach also allows for the appropriate dimensionality of the latent space to be automatically determined. We demonstrate the model on a human motion capture data set and a series of high resolution video sequences.

Author Information

Andreas Damianou (University of Sheffield)
Michalis Titsias (Athens University of Economics and Business)
Neil D Lawrence (University of Cambridge)

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