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arXiv · 1811.07627

Mixed Likelihood Gaussian Process Latent Variable Model

Samuel Murray·Hedvig Kjellström
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Abstract

We present the Mixed Likelihood Gaussian process latent variable model (GP-LVM), capable of modeling data with attributes of different types. The standard formulation of GP-LVM assumes that each observation is drawn from a Gaussian distribution, which makes the model unsuited for data with e.g. categorical or nominal attributes. Our model, for which we use a sampling based variational inference, instead assumes a separate likelihood for each observed dimension. This formulation results in more meaningful latent representations, and give better predictive performance for real world data with dimensions of different types.

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Samuel Murray, Hedvig Kjellström. 2018-11-19. Mixed Likelihood Gaussian Process Latent Variable Model. https://arxiv.org/abs/1811.07627

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