{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dolda-a-regularized-supervised-topic-model","title":"DOLDA - a regularized supervised topic model for high-dimensional multi-class regression","arxiv_id":"1602.00260","date":"2016-01-31","proceeding":null,"authors":["Måns Magnusson","Leif Jonsson","Mattias Villani"],"abstract":"Generating user interpretable multi-class predictions in data rich\nenvironments with many classes and explanatory covariates is a daunting task.\nWe introduce Diagonal Orthant Latent Dirichlet Allocation (DOLDA), a supervised\ntopic model for multi-class classification that can handle both many classes as\nwell as many covariates. To handle many classes we use the recently proposed\nDiagonal Orthant (DO) probit model (Johndrow et al., 2013) together with an\nefficient Horseshoe prior for variable selection/shrinkage (Carvalho et al.,\n2010). We propose a computationally efficient parallel Gibbs sampler for the\nnew model. An important advantage of DOLDA is that learned topics are directly\nconnected to individual classes without the need for a reference class. We\nevaluate the model's predictive accuracy on two datasets and demonstrate\nDOLDA's advantage in interpreting the generated predictions.","url_abs":"http://arxiv.org/abs/1602.00260v2","url_pdf":"http://arxiv.org/pdf/1602.00260v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dolda-a-regularized-supervised-topic-model","repo_url":"https://github.com/lejon/DiagonalOrthantLDA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-class-classification","task_name":"Multi-class Classification"},{"task_slug":"variable-selection","task_name":"Variable Selection"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1602.00260","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}