Gaussian Process Topic Models

@inproceedings{Agovic2010GaussianPT,
  title={Gaussian Process Topic Models},
  author={Amrudin Agovic and Arindam Banerjee},
  booktitle={UAI},
  year={2010}
}
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… CONTINUE READING
Highly Cited
This paper has 22 citations. REVIEW CITATIONS
Recent Discussions
This paper has been referenced on Twitter 1 time over the past 90 days. VIEW TWEETS

Citations

Publications citing this paper.
Showing 1-10 of 14 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 13 references

Iterative solution of the Lyapunov matrix equation

  • E. L. Wachspress
  • Appl. Math. Lett., 1(1):87–90
  • 1988
3 Excerpts

Similar Papers

Loading similar papers…