Edoardo Airoldi, David Blei, Eric Xing {eairoldi,blei,xing}@cs.cmu.edu...  Home/Search
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Abstract: data analysis and machine learning. In this paper we propose a Bayesian model that uses a hierarchy of probabilistic assumptions about the way objects interact with one another in order to learn latent groups, their typical interaction patterns, and the degree of membership of objects to groups. Our model explains the data using a small set of parameters that can be reliably estimated with an e#cient inference algorithm. In our approach, the set of probabilistic assumptions may be tailored to a ... (Update)
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BibTeX entry: (Update)
@misc{ david-latent,
author = "Edoardo Airoldi David",
title = "A Latent Mixed Membership Model for Relational Data",
url = "citeseer.ist.psu.edu/762357.html" }
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