%0 Conference Paper %1 citeulike:401262 %A Minka, Thomas %A Lafferty, John %B Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence %D 2002 %K topicinference socialnets %P 352--359 %T Expectation-Propagation for the Generative Aspect Model %U http://research.microsoft.com/~minka/papers/aspect/minka-aspect.pdf %X 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.

@inproceedings{citeulike:401262, abstract = {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.}, added-at = {2006-06-16T10:34:37.000+0200}, author = {Minka, Thomas and Lafferty, John}, biburl = {https://www.bibsonomy.org/bibtex/27680d22f1349a4f96ddbf178d9547e1c/ldietz}, booktitle = {Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence}, citeulike-article-id = {401262}, comment = {"It is found that the variational methods can lead to inaccurate inferences and biased learning, while Expectation- Propagation gives results that are more true to the model." --- re-read!}, interhash = {b4be319c9424a60c9d3bec9fe535b3ed}, intrahash = {7680d22f1349a4f96ddbf178d9547e1c}, keywords = {topicinference socialnets}, pages = {352--359}, priority = {4}, timestamp = {2006-06-16T10:34:37.000+0200}, title = {Expectation-Propagation for the Generative Aspect Model}, url = {http://research.microsoft.com/~minka/papers/aspect/minka-aspect.pdf}, year = 2002 }