Structured Bayesian Gaussian process latent variable model [article]

Steven Atkinson, Nicholas Zabaras
2018 arXiv   pre-print
We introduce a Bayesian Gaussian process latent variable model that explicitly captures spatial correlations in data using a parameterized spatial kernel and leveraging structure-exploiting algebra on the model covariance matrices for computational tractability. Inference is made tractable through a collapsed variational bound with similar computational complexity to that of the traditional Bayesian GP-LVM. Inference over partially-observed test cases is achieved by optimizing a
more » ... psed" bound. Modeling high-dimensional time series systems is enabled through use of a dynamical GP latent variable prior. Examples imputing missing data on images and super-resolution imputation of missing video frames demonstrate the model.
arXiv:1805.08665v1 fatcat:fftbpnsavrdsdfzblpnplclkcu