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We present a non-parametric Bayesian latent variable model capable oflearning dependency structures across dimensions in a multivariate setting. Ourapproach is based on flexible Gaussian process priors for the generativemappings and interchangeable Dirichlet process priors to learn the structure.The introduction of the Dirichlet process as a specific structural prior allowsour model to circumvent issues associated with previous Gaussian process latentvariable models. Inference is performed by deriving an efficient variationalbound on the marginal log-likelihood on the model.

DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures