Bayesian Gaussian Process Latent Variable Model


We introduce a variational inference framework for training the Gaussian process latent variable model and thus performing Bayesian nonlinear dimensionality reduction. This method allows us to variationally integrate out the input variables of the Gaussian process and compute a lower bound on the exact marginal likelihood of the nonlinear latent variable model. The maxi-mization of the variational lower bound provides a Bayesian training procedure that is robust to overfitting and can automatically select the di-mensionality of the nonlinear latent space. We demonstrate our method on real world datasets. The focus in this paper is on dimensionality reduction problems, but the methodology is more general. For example, our algorithm is immediately applicable for training Gaussian process models in the presence of missing or uncertain inputs.

Extracted Key Phrases

3 Figures and Tables

Showing 1-10 of 83 extracted citations
Highly Influenced
8 Excerpts
Citations per Year

217 Citations

Semantic Scholar estimates that this publication has received between 153 and 303 citations based on the available data.

See our FAQ for additional information.