The Indian Buffet Process: Scalable Inference and Extensions
Finale Doshi‐Velez · 2009
August 2009This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except where specifically indicated in the text. c ○ Copyright by Finale Doshi-Velez, 2009. 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: