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Published in:
2011 | OriginalPaper | Chapter
Typology of Mixed-Membership Models: Towards a Design Method
Author : Gregor Heinrich
Published in: Machine Learning and Knowledge Discovery in Databases
Publisher: Springer Berlin Heidelberg
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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.