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

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Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 36th International Conference on Machine Learning
EditorsKamalika Chaudhuri, Ruslan Salakhutdinov
Place of PublicationLong Beach, California, USA
PublisherPMLR
Pages3682-3691
Number of pages10
Volume97
Publication statusPublished - 9 Jun 2019
EventThirty-sixth International Conference on Machine Learning - Long Beach Convention Center, Long Beach, USA United States
Duration: 9 Jun 201915 Jun 2019
Conference number: 36
https://icml.cc/

Publication series

NameProceedings of Machine Learning Research
PublisherPMLR
ISSN (Electronic)2368-5417

Conference

ConferenceThirty-sixth International Conference on Machine Learning
Abbreviated titleICML
CountryUSA United States
CityLong Beach
Period9/06/1915/06/19
Internet address

Cite this

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.