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A generic approach to topic models

: Heinrich, Gregor


Buntine, W.:
Machine learning and knowledge discovery in databases. Proceedings. Vol.1 : European conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009
Berlin: Springer, 2009 (Lecture Notes in Computer Science 5781)
ISBN: 3-642-04179-5
ISBN: 978-3-642-04179-2
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) <2009, Bled>
Conference Paper
Fraunhofer IGD ()
artificial intelligence (AI); machine learning; topic model

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 domainspecific compact alternative to Bayesian networks. Besides illustrating the interconnection of mixtures in topic models, the benefit of this representation is ist straight-forward mapping to inference equations and algorithms, which is shown with the derivation and implementation of a generic Gibbs sampling algorithm.