Speaker |
Tamara Broderick |
|---|---|
Affiliation |
University of California, Berkeley |
Date |
Friday, 01 March 2013 |
Time |
12:30-14:00 |
Location |
Zoom |
Link |
Roberts 421 |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
The problem of inferring a clustering of a data set has been the subject of much research in Bayesian analysis, and there currently exists a solid mathematical foundation for Bayesian approaches to clustering. In particular, the class of probability distributions over partitions of a data set has been characterized in a number of ways, including via exchangeable partition probability functions (EPPFs) and the Kingman paintbox. Here, we develop a generalization of the clustering problem, called feature allocation, where we allow each data point to belong to an arbitrary, non-negative integer number of groups, now called features or topics. We define and study an "exchangeable feature probability function" (EFPF)---analogous to the EPPF in the clustering setting---for certain types of feature models. Moreover, we introduce a "feature paintbox" characterization---analogous to the Kingman paintbox for clustering---of the class of exchangeable feature models. We use this feature paintbox construction to provide a further characterization of the subclass of feature allocations that have EFPF representations. Slides for the talk: PDF |
Biography |