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On the integration of topic modeling and dictionary learning

Publication ,  Journal Article
Li, L; Zhou, M; Sapiro, G; Carin, L
Published in: Proceedings of the 28th International Conference on Machine Learning, ICML 2011
October 7, 2011

A new nonparametric Bayesian model is developed to integrate dictionary learning and topic model into a unified framework. The model is employed to analyze partially annotated images, with the dictionary learning performed directly on image patches. Efficient inference is performed with a Gibbs-slice sampler, and encouraging results are reported on widely used datasets. Copyright 2011 by the author(s)/owner(s).

Duke Scholars

Published In

Proceedings of the 28th International Conference on Machine Learning, ICML 2011

Publication Date

October 7, 2011

Start / End Page

625 / 632
 

Citation

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Li, L., Zhou, M., Sapiro, G., & Carin, L. (2011). On the integration of topic modeling and dictionary learning. Proceedings of the 28th International Conference on Machine Learning, ICML 2011, 625–632.
Li, L., M. Zhou, G. Sapiro, and L. Carin. “On the integration of topic modeling and dictionary learning.” Proceedings of the 28th International Conference on Machine Learning, ICML 2011, October 7, 2011, 625–32.
Li L, Zhou M, Sapiro G, Carin L. On the integration of topic modeling and dictionary learning. Proceedings of the 28th International Conference on Machine Learning, ICML 2011. 2011 Oct 7;625–32.
Li, L., et al. “On the integration of topic modeling and dictionary learning.” Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Oct. 2011, pp. 625–32.
Li L, Zhou M, Sapiro G, Carin L. On the integration of topic modeling and dictionary learning. Proceedings of the 28th International Conference on Machine Learning, ICML 2011. 2011 Oct 7;625–632.

Published In

Proceedings of the 28th International Conference on Machine Learning, ICML 2011

Publication Date

October 7, 2011

Start / End Page

625 / 632