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

arXiv: https://arxiv.org/abs/1602.00260 · HTML: https://mlsanity.com/paper/1602.00260/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2016.

## Built on by (most cited first, 2 of 2)

- [Bayesian Topic Regression for Causal Inference](https://mlsanity.com/paper/2109.05317/) (2021)
- [Fine-Tuning Topics through Weighting Aspect Keywords](https://mlsanity.com/paper/2502.08496/) (2025)

## Abstract (arXiv)

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 both many classes as well as many covariates. To handle many classes we use the recently proposed Diagonal Orthant (DO) probit model (Johndrow et al., 2013) together with an efficient Horseshoe prior for variable selection/shrinkage (Carvalho et al., 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 on two datasets and demonstrate DOLDA's advantage in interpreting the generated predictions.