Tom Minka - joint work with John Lafferty.
The generative aspect model is an extension of the multinomial model
for text that allows word probabilities to vary stochastically across
documents. Previous results with aspect models have been promising,
but hindered by the computational difficulty of carrying out inference
and learning. This paper demonstrates that the simple variational
methods of Blei et al (2001) can lead to inaccurate inferences and
biased learning for the generative aspect model. We develop an
alternative approach that leads to higher accuracy at comparable
cost. An extension of Expectation-Propagation is used for inference
and then embedded in an EM algorithm for learning. Experimental
results are presented for both synthetic and real data sets.
The paper appeared in the Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence 2002.
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Charles Rosenberg Last modified: Fri Aug 30 23:14:52 EDT 2002