Abstract by Jeremy Meyer
The Attraction Indian Buffet Distribution
Latent feature models have been used to model many problems with a high number of predictor variables and few observations. The Indian Buffet process (IBP) is a popular Bayesian nonparametric prior for a latent feature allocation. This prior assumes exchangeability, however there are cases where it is practical to use prior distance information among observations for better results. We propose the attraction Indian Buffet distribution (AIBD), a generalization of the IBP that allows pairwise similarities to influence the sharing of features a priori. We explore the properties of the AIBD and suggest ways the AIBD can leverage the pairwise distance information for improved feature allocation estimation.