Article

Gaussian Process Topic Models

Authors:

Amrudin Agovic,

Arindam BanerjeeAuthors Info & Claims

UAI'10: Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence

Pages 10 - 19

Published: 08 July 2010 Publication History

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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.

References

[1]

[Bartels and Stewart(1972)] R. H. Bartels and G. W. Stewart. Solution of the matrix equation AX + XB = C. Commun. ACM, 15(9):820-826, 1972.

Crossref

Google Scholar

[2]

[Blei and Lafferty(2006)] D. Blei and J. Lafferty. Correlated Topic Models. In NIPS, 18:147, 2006.

Crossref

Google Scholar

[3]

[Blei et al.(2003)] D. Blei, A. Ng and M. Jordan. Latent Dirichlet allocation. JMLR, 3:993-1022, 2003.

Crossref

Google Scholar

[4]

[Chang and Blei(2009)] J. Chang and D. Blei. Relational topic models for document networks. In AISTATS, 2009.

Google Scholar

[5]

[Foster et al.(2009)] L. Foster, A. Waagen, N. Aijaz, M. Hurley, A. Luis, J. Rinsky and C. Satyavolu. Stable and efficient Gaussian process calculations. JMLR, 10:857-882, 2009.

Crossref

Google Scholar

[6]

[Golub et al.(1979)] G. Golub, S. Nash and C. V. Loan. A Hessenberg-Schur method for the problem AX + XB= C. IEEE Transactions on Automatic Control, 24(6), 1979.

Google Scholar

[7]

[Griffiths and Steyvers(2004)] T. L. Griffiths and M. Steyvers. Finding scientific topics. PNAS, 101:5228-5235, 2004.

Google Scholar

[8]

[Lawrence(2003)] N. Lawrence. Gaussian process latent variable models for visualisation of high dimensional data. In NIPS, 2003.

Crossref

Google Scholar

[9]

[Lawrence et al.(2002)] N. D. Lawrence, M. Seeger and R. Herbrich. Fast sparse Gaussian process methods: The informative vector machine. In NIPS, 2002.

Crossref

Google Scholar

[10]

[Candela and Rasmussen(2005)] J. Q. Candela and C. E. Rasmussen. A unifying view of sparse approximate Gaussian process regression. JMLR, 6:1939-1959, 2005.

Crossref

Google Scholar

[11]

[Rasmussen and Williams(2005)] C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. The MIT Press, 2005.

Crossref

Google Scholar

[12]

[Wachspress(1988)] E. L. Wachspress. Iterative solution of the Lyapunov matrix equation. Appl. Math. Lett., 1(1):87-90, 1988.

Crossref

Google Scholar

Cited By

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Liu LHuang HGao YZhang YWei X(2019)Neural Variational Correlated Topic ModelingThe World Wide Web Conference10.1145/3308558.3313561(1142-1152)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313561
Gaur MKursuncu UAlambo ASheth ADaniulaityte RThirunarayan KPathak JCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)"Let Me Tell You About Your Mental Health!"Proceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271732(753-762)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3271732
Liu YZhou BChen FCheung DMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Graph Topic Scan Statistic for Spatial Event DetectionProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983744(489-498)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983744
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Index Terms

Gaussian Process Topic Models
1. Computing methodologies
  1. Machine learning

Index terms have been assigned to the content through auto-classification.

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Published In

UAI'10: Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence

July 2010

751 pages

ISBN:9780974903965

Editors:
Peter Grunwald
Advanced Systems Research, CWI, Amsterdam, Netherlands
,
Peter Spirtes
Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA

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AUAI Press

Arlington, Virginia, United States

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Published: 08 July 2010

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Liu LHuang HGao YZhang YWei X(2019)Neural Variational Correlated Topic ModelingThe World Wide Web Conference10.1145/3308558.3313561(1142-1152)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308558.3313561
Gaur MKursuncu UAlambo ASheth ADaniulaityte RThirunarayan KPathak JCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)"Let Me Tell You About Your Mental Health!"Proceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271732(753-762)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3271732
Liu YZhou BChen FCheung DMukhopadhyay SZhai CBertino ECrestani FMostafa JTang JSi LZhou XChang YLi YSondhi P(2016)Graph Topic Scan Statistic for Spatial Event DetectionProceedings of the 25th ACM International on Conference on Information and Knowledge Management10.1145/2983323.2983744(489-498)Online publication date: 24-Oct-2016
https://dl.acm.org/doi/10.1145/2983323.2983744
El Ghaoui LPham VLi GDuong VSrivastava ABhaduri K(2013)Understanding large text corpora via sparse machine learningStatistical Analysis and Data Mining10.1002/sam.111876:3(221-242)Online publication date: 1-Jun-2013
https://dl.acm.org/doi/10.1002/sam.11187

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Topic models with power-law using Pitman-Yor process

Topic analysis for topic-focused multi-document summarization

Topic sentiment change analysis

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