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DOLDA: a regularized supervised topic model for high-dimensional multi-class regression
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences. Aalto University, Espoo, Finland.
Ericsson AB, Stockholm, Sweden.
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences. Stockholm University, Stockholm, Sweden.
2019 (English)In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658Article in journal (Refereed) Epub ahead of print
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2019.
Keywords [en]
Text classification, Latent Dirichlet Allocation, Horseshoe prior, Diagonal Orthant probit model, Interpretable models
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-159217DOI: 10.1007/s00180-019-00891-1Scopus ID: 2-s2.0-85067414496OAI: oai:DiVA.org:liu-159217DiVA, id: diva2:1340533
Available from: 2019-08-05 Created: 2019-08-05 Last updated: 2019-11-14Bibliographically approved

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