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.
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