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

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Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2020-03-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
27
Series
Computational Statistics
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.
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Keywords
Diagonal Orthant probit model, Horseshoe prior, Interpretable models, Latent Dirichlet Allocation, Text classification
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Citation
Magnusson, M, Jonsson, L & Villani, M 2020, ' DOLDA : a regularized supervised topic model for high-dimensional multi-class regression ', Computational Statistics, vol. 35, no. 1, pp. 175-201 . https://doi.org/10.1007/s00180-019-00891-1