PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems
David Barber and Bertrand Mesot
In: Advances in Neural Information Processing Systems NIPS 20, Dec 2006, Vancouver, Canada.


We introduce amethod for approximate smoothed inference in a class of switching linear dynamical systems, based on a novel form of Gaussian Sum smoother. This class includes the switching Kalman Filter and the more general case of switch transitions dependent on the continuous latent state. The method improves on the standard Kim smoothing approach by dispensing with one of the key approximations, thus making fuller use of the available future information. Whilst the only central assumption required is projection to a mixture of Gaussians, we show that an additional conditional independence assumption results in a simpler but accurate alternative. Unlike the alternative Expectation Propagation procedure, our method consists only of a single forward and backward pass and is reminiscent of the standard smoothing ‘correction’ recursions in the simpler linear dynamical system. The algorithm performs well on both toy experiments and in a large scale application to noise robust speech recognition.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3797
Deposited By:David Barber
Deposited On:25 February 2008