Uncertainty in Artificial Intelligence
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Gaussian Process Topic Models
Amrudin Agovic, Arindam Banerjee
Abstract:
We introduce Gaussian Process Topic Models (GPTMs), a new family of topic models which can leverage a kernel among documents while extracting correlated topics. GPTMs can be considered a systematic generalization of the Correlated Topic Models (CTMs) using ideas from Gaussian Process (GP) based embedding. Since GPTMs work with both a topic covariance matrix and a document kernel matrix, learning GPTMs involves a novel component-solving a suitable Sylvester equation capturing both topic and document dependencies. The efficacy of GPTMs is demonstrated with experiments evaluating the quality of both topic modeling and embedding.
Keywords:
Pages: 10-19
PS Link:
PDF Link: /papers/10/p10-agovic.pdf
BibTex:
@INPROCEEDINGS{Agovic10,
AUTHOR = "Amrudin Agovic and Arindam Banerjee",
TITLE = "Gaussian Process Topic Models",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "10--19"
}


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