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Typology of Mixed-Membership Models: Towards a Design Method

  • Gregor Heinrich
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6912)

Abstract

Presents an analysis of the structure of mixed-membership models into elementary blocks and their numerical properties. By associating such model structures with structures known or assumed in the data, we propose how models can be constructed in a controlled way, using the numerical properties of data likelihood and Gibbs full conditionals as predictors of model behavior. To illustrate this “bottom-up” design method, example models are constructed that may be used for expertise finding from labeled data.

Keywords

Bayesian Network Gibbs Sampling Topic Model Latent Dirichlet Allocation Numerical Property 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gregor Heinrich
    • 1
  1. 1.University of Leipzig + vsonix GmbHDarmstadtGermany

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