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

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

Diagonal Orthant probit model, Horseshoe prior, Interpretable models, Latent Dirichlet Allocation, Text classification

DOI

10.1007/s00180-019-00891-1

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

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https://urn.fi/URN:NBN:fi:aalto-201907304471

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[article-cris] Perustieteiden korkeakoulu / SCI

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

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