An Alternative Infinite Mixture Of Gaussian Process Experts

Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)

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Authors

Edward Meeds, Simon Osindero

Abstract

We present an infinite mixture model in which each component com- prises a multivariate Gaussian distribution over an input space, and a Gaussian Process model over an output space. Our model is neatly able to deal with non-stationary covariance functions, discontinuities, multi- modality and overlapping output signals. The work is similar to that by Rasmussen and Ghahramani [1]; however, we use a full generative model over input and output space rather than just a conditional model. This al- lows us to deal with incomplete data, to perform inference over inverse functional mappings as well as for regression, and also leads to a more powerful and consistent Bayesian specification of the effective ‘gating network’ for the different experts.