A simple non-parametric Topic Mixture for Authors and Documents

2012/11/27 by Arnim Bleier, Bleier, Arnim
Computer Science · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1211.6248

arxiv created 2012/12/04 · arxiv updated 2012/12/05

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.

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