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

Producción científica: Conference contribution

3 Citas (Scopus)

Resumen

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.

Idioma originalEnglish (US)
Título de la publicación alojadaPRNI 2016 - 6th International Workshop on Pattern Recognition in Neuroimaging
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781467365307
DOI
EstadoPublished - ago 24 2016
Evento6th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2016 - Trento, Italy
Duración: jun 22 2016jun 24 2016

Serie de la publicación

NombrePRNI 2016 - 6th International Workshop on Pattern Recognition in Neuroimaging

Other

Other6th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2016
País/TerritorioItaly
CiudadTrento
Período6/22/166/24/16

ASJC Scopus subject areas

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

Huella

Profundice en los temas de investigación de 'Collapsed variational Bayesian inference of the author-topic model: Application to large-scale coordinate-based meta-analysis'. En conjunto forman una huella única.

Citar esto