The Indian Buffet Process: Scalable Inference and Extensions
Masters thesis, University of Cambridge.
Many unsupervised learning problems seek to identify hidden features from observations. In many real-world situations, the number of hidden features is unknown. To avoid specifying the number of hidden features a priori, one can use the Indian Buffet Process (IBP): a nonparametric latent feature model that does not bound the number of active features in a dataset. While elegant, the lack of efficient inference procedures for the IBP has prevented its application in large-scale problems. The core contribution of this thesis are three new inference procedures that allow inference in the IBP to be scaled from a few hundred to 100,000 observations.
This thesis contains three parts:
(1) An introduction to the IBP and a review of inference techniques and extensions. The first chapters summarise three constructions for the IBP and review all currently published inference techniques. Appendices review extensions of the IBP to date.
(2) Novel techniques for scalable Bayesian inference. This thesis presents three new inference procedures: (a) an accelerated Gibbs sampler for efficient Bayesian inference in a broad class of conjugate models, (b) a parallel, asynchronous Gibbs sampler that allows the accelerated Gibbs sampler to be distributed across multiple processors, and (c) a variational inference procedure for the IBP.
(3) A framework for structured nonparametric latent feature models. We also present extensions to the IBP to model more sophisticated relationships between the co-occurring hidden features, providing a general framework for correlated non-parametric feature models.