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.
Gaussian Process Topic Models (GPTMs) by Agovic and Banerjee REF is a novel family of topic models which define a Gaussian Process Mapping from the document space into the topic space.
Gaussian Process Topic Models
ArXiv
ArXiv
Mathematics Computer Science