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.
|Title of host publication||Proceedings of the 36th International Conference on Machine Learning|
|Editors||Kamalika Chaudhuri, Ruslan Salakhutdinov|
|Place of Publication||Long Beach, California, USA|
|Number of pages||10|
|Publication status||Published - 9 Jun 2019|
|Event||Thirty-sixth International Conference on Machine Learning - Long Beach Convention Center, Long Beach, USA United States|
Duration: 9 Jun 2019 → 15 Jun 2019
Conference number: 36
|Name||Proceedings of Machine Learning Research|
|Conference||Thirty-sixth International Conference on Machine Learning|
|Country||USA United States|
|Period||9/06/19 → 15/06/19|
Lawrence, A., Ek, C. H., & Campbell, N. (2019). DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures. In K. Chaudhuri, & R. Salakhutdinov (Eds.), Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 3682-3691). (Proceedings of Machine Learning Research). Long Beach, California, USA: PMLR.