Gaussian Process Topic Models
Machine Learning
2012-03-19 v1 Machine Learning
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.
Cite
@article{arxiv.1203.3462,
title = {Gaussian Process Topic Models},
author = {Amrudin Agovic and Arindam Banerjee},
journal= {arXiv preprint arXiv:1203.3462},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010)