Machine Learning

Latent Gaussian Process Regression

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  • arXiv
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    Latent Gaussian Process Regression

    We introduce Latent Gaussian Process Regression which is a latent variable extension allowing modelling of non-stationary multi-modal processes using GPs. The approach is built on extending the input space of a regression problem with a latent variable that is used to modulate the covariance function over the training data. We show how our approach can be used to model multi-modal and non-stationary processes. We exemplify the approach on a set of synthetic data and provide results on real data from motion capture and geostatistics.

    Latent Gaussian Process Regression
    by Erik Bodin, Neill D. F. Campbell, Carl Henrik Ek
    https://arxiv.org/pdf/1707.05534v2.pdf

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