Center for Automated Learning and Discovery
School of Computer Science, Carnegie Mellon University


On Topic Evolution

Eric P. Xing

December 2005


Keywords: Dirichlet Process, nonparametric Bayesian models, birth-death process, Kalman filter, state-space models, longitudinal data analysis

I introduce topic evolution models for longitudinal epochs of word documents. The models employ marginally dependent latent state-space models for evolving topic proportion distributions and topicspecific word distributions; and either a logistic-normal-multinomial or a logistic-normal-Poisson model for document likelihood. These models allow posterior inference of latent topic themes over time, and topical clustering of longitudinal document epochs. I derive a variational inference algorithm for nonconjugate generalized linear models based on truncated Taylor approximation, and I also outline formulae for parameter estimation based on variational EM principle.

18 pages

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