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Spherical Topic Models (2009)
Joseph Reisinger
,
Austin Waters
,
Bryan Silverthorn
, and
Raymond Mooney
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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Citation:
In
NIPS'09 workshop: Applications for Topic Models: Text and Beyond
, 2009.
Bibtex:
@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://nn.cs.utexas.edu/?reisinger:nips09", year={2009} }
People
Raymond J. Mooney
mooney [at] cs utexas edu
Joseph Reisinger
Former 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
Areas of Interest
Text Categorization and Clustering
Text Data Mining
Unsupervised Learning, Clustering, and Self-Organization