DOLDA: a regularized supervised topic model for high-dimensional multi-class regression

Måns Magnusson (), Leif Jonsson () and Mattias Villani
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Måns Magnusson: Linköping University
Leif Jonsson: Ericsson AB

Computational Statistics, 2020, vol. 35, issue 1, No 12, 175-201

Abstract: Abstract Generating user interpretable multi-class predictions in data-rich environments with many classes and explanatory covariates is a daunting task. We introduce Diagonal Orthant Latent Dirichlet Allocation (DOLDA), a supervised topic model for multi-class classification that can handle many classes as well as many covariates. To handle many classes we use the recently proposed Diagonal Orthant probit model (Johndrow et al., in: Proceedings of the sixteenth international conference on artificial intelligence and statistics, 2013) together with an efficient Horseshoe prior for variable selection/shrinkage (Carvalho et al. in Biometrika 97:465–480, 2010). We propose a computationally efficient parallel Gibbs sampler for the new model. An important advantage of DOLDA is that learned topics are directly connected to individual classes without the need for a reference class. We evaluate the model’s predictive accuracy and scalability, and demonstrate DOLDA’s advantage in interpreting the generated predictions.

Keywords: Text classification; Latent Dirichlet Allocation; Horseshoe prior; Diagonal Orthant probit model; Interpretable models (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s00180-019-00891-1

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