Gaussian Process Topic Models

  title={Gaussian Process Topic Models},
  author={Amrudin Agovic and Arindam Banerjee},
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
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Iterative solution of the Lyapunov matrix equation

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