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X-WR-CALNAME;VALUE=TEXT:MachineLearn: Tamara Broderick
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SUMMARY:MachineLearn: Tamara Broderick
DESCRIPTION:Feature allocations, probability functions, and paintboxesThe 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.
LOCATION:Maxwell-Dworkin G125
STATUS:CONFIRMED
DTSTART:20131106T210000Z
DTEND:20131106T220000Z
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