Tutorial on Probabilistic Topic Modeling: Additive Regularization for Stochastic Matrix Factorization
Konstantin Vyacheslavovich Vorontsov, Anna Potapenko · 2014
Abstract. Probabilistic topic modeling of text collections is a powerful tool for statistical text analysis. In this tutorial we introduce a novel non-Bayesian approach, called Additive Regularization of Topic Models. ARTM is free of redundant probabilistic assumptions and provides a simple inference for many combined and multi-objective topic models.