A Generic Approach to Topic Models
Abstract
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.
Cite
Text
Heinrich. "A Generic Approach to Topic Models." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009. doi:10.1007/978-3-642-04180-8_51Markdown
[Heinrich. "A Generic Approach to Topic Models." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009.](https://mlanthology.org/ecmlpkdd/2009/heinrich2009ecmlpkdd-generic/) doi:10.1007/978-3-642-04180-8_51BibTeX
@inproceedings{heinrich2009ecmlpkdd-generic,
title = {{A Generic Approach to Topic Models}},
author = {Heinrich, Gregor},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2009},
pages = {517-532},
doi = {10.1007/978-3-642-04180-8_51},
url = {https://mlanthology.org/ecmlpkdd/2009/heinrich2009ecmlpkdd-generic/}
}