DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures
Carl Henrik Ek, Andrew R. Lawrence, Neill D. F. Campbell · arXiv (Cornell University) · 2018
We present a non-parametric Bayesian latent variable model capable of learning dependency structures across dimensions in a multivariate setting.Our approach is based on flexible Gaussian process priors for the generative mappings and interchangeable Dirichlet process priors to learn the structure. The introduction of the Dirichlet process as a specific structural prior allows our model to circumvent issues associated with previous Gaussian process latent variable models. Inference is performed by deriving an efficient variational bound on the marginal log-likelihood of the model. We demonstrate the efficacy of our approach via analysis of discovered structure and superior quantitative performance on missing data imputation.