5585

A Generic Approach to Topic Models

Gregor Heinrich
Fraunhofer IGD
Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science, Volume 5781/2009, 517-532, 2009

@article{heinrich2009generic,

   title={A generic approach to topic models},

   author={Heinrich, G.},

   journal={Machine Learning and Knowledge Discovery in Databases},

   pages={517–532},

   year={2009},

   publisher={Springer}

}

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This article contributes a generic model of topic models. To define the problem space, general characteristics for this class of models are derived, which give rise to a representation of topic models as "mixture networks", a domain-specific compact alternative to Bayesian networks. Besides illustrating the interconnection of mixtures in topic models, the benefit of this representation is its straight-forward mapping to inference equations and algorithms, which is shown with the derivation and implementation of a generic Gibbs sampling algorithm.
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