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.

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In NIPS'09 workshop: Applications for Topic Models: Text and Beyond 2009.

@inproceedings{reisinger:nips09, title={Spherical Topic Models}, author={Joseph Reisinger and Austin Waters and Bryan Silverthorn and Raymond Mooney}, booktitle={NIPS'09 workshop: Applications for Topic Models: Text and Beyond}, url="http://www.cs.utexas.edu/users/ai-lab?reisinger:nips09", year={2009} }