A simple non-parametric Topic Mixture for Authors and Documents
Machine Learning
2012-12-05 v2 Machine Learning
Abstract
This article reviews the Author-Topic Model and presents a new non-parametric extension based on the Hierarchical Dirichlet Process. The extension is especially suitable when no prior information about the number of components necessary is available. A blocked Gibbs sampler is described and focus put on staying as close as possible to the original model with only the minimum of theoretical and implementation overhead necessary.
Keywords
Cite
@article{arxiv.1211.6248,
title = {A simple non-parametric Topic Mixture for Authors and Documents},
author = {Arnim Bleier},
journal= {arXiv preprint arXiv:1211.6248},
year = {2012}
}