%0 Conference Paper %1 can09 %A Canini, Kevin R. %A Shi, Lei %A Griths, Thomas L. %B Proceedings of AI Stats %D 2009 %K algorithms lda master toVerify topics %T Online Inference of Topics with Latent Dirichlet Allocation %X Inference algorithms for topic models are typ- ically designed to be run over an entire col- lection of documents after all of the docu- ments have been observed. However, in many applications of these models, the collection of documents grows in size over time, mak- ing it infeasible to run batch algorithms re- peatedly. This problem can be addressed by using online inference algorithms, which up- date estimates of the topics as each document is observed. We introduce two related Rao- Blackwellized online inference algorithms for the latent Dirichlet allocation (LDA) model  incremental Gibbs samplers and particle
l- ters  and compare their runtime and perfor- mance to that of existing algorithms.

@inproceedings{can09, abstract = {Inference algorithms for topic models are typ- ically designed to be run over an entire col- lection of documents after all of the docu- ments have been observed. However, in many applications of these models, the collection of documents grows in size over time, mak- ing it infeasible to run batch algorithms re- peatedly. This problem can be addressed by using online inference algorithms, which up- date estimates of the topics as each document is observed. We introduce two related Rao- Blackwellized online inference algorithms for the latent Dirichlet allocation (LDA) model  incremental Gibbs samplers and particle
l- ters  and compare their runtime and perfor- mance to that of existing algorithms.}, added-at = {2010-05-11T22:18:16.000+0200}, author = {Canini, Kevin R. and Shi, Lei and Griths, Thomas L.}, biburl = {https://www.bibsonomy.org/bibtex/2f75cb2a300a8f382732dde4615289bb8/ans}, booktitle = {Proceedings of AI Stats}, interhash = {c622ade04f9c612c9dae4f0c4a67a514}, intrahash = {f75cb2a300a8f382732dde4615289bb8}, keywords = {algorithms lda master toVerify topics}, timestamp = {2011-03-22T23:02:17.000+0100}, title = {Online Inference of Topics with Latent Dirichlet Allocation}, year = 2009 }