Collapsed variational Bayesian inference of the author-topic model: Application to large-scale coordinate-based meta-analysis

Gia H. Ngo, Simon B. Eickhoff, Peter T Fox, B. T Thomas Yeo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The author-topic (AT) model has been recently used to discover the relationships between brain regions, cognitive components and behavioral tasks in the human brain. In this work, we propose a novel Collapsed Variational Bayesian (CVB) inference algorithm for the AT model. The proposed algorithm is compared with the Expectation-Maximization (EM) algorithm on the large-scale BrainMap database of brain activation coordinates and behavioral tasks. Experiments suggest that the proposed CVB algorithm produces parameter estimates with better generalization power than the EM algorithm.

Original languageEnglish (US)
Title of host publicationPRNI 2016 - 6th International Workshop on Pattern Recognition in Neuroimaging
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467365307
DOIs
StatePublished - Aug 24 2016
Event6th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2016 - Trento, Italy
Duration: Jun 22 2016Jun 24 2016

Other

Other6th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2016
CountryItaly
CityTrento
Period6/22/166/24/16

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Biomedical Engineering

Cite this

Ngo, G. H., Eickhoff, S. B., Fox, P. T., & Yeo, B. T. T. (2016). Collapsed variational Bayesian inference of the author-topic model: Application to large-scale coordinate-based meta-analysis. In PRNI 2016 - 6th International Workshop on Pattern Recognition in Neuroimaging [7552332] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PRNI.2016.7552332