Spherical Topic Models (2009)
We introduce the Spherical Admixture Model (SAM), a Bayesian topic model over arbitrary L2 normalized data. SAM models documents as points on a high- dimensional spherical manifold, and is capable of representing negative word- topic correlations and word presence/absence, unlike models with multinomial document likelihood, such as LDA. In this paper, we evaluate SAM as a topic browser, focusing on its ability to model “negative” topic features, and also as a dimensionality reduction method, using topic proportions as features for difficult classification tasks in natural language processing and computer vision.
In NIPS'09 workshop: Applications for Topic Models: Text and Beyond 2009.

Raymond J. Mooney Faculty mooney [at] cs utexas edu
Joseph Reisinger Ph.D. Alumni joeraii [at] cs utexas edu
Joseph Reisinger Formerly affiliated Ph.D. Student joeraii [at] cs utexas edu
Bryan Silverthorn Ph.D. Alumni bsilvert [at] cs utexas edu
Austin Waters Ph.D. Alumni austin [at] cs utexas edu