The high Dirichlet priors are chosen to prevent sparsity in topic distributions , while the other parameters are selected as the best from Hoffman et al. ( 2010 ) .
online learning for latent dirichlet allocation
Matthew Hoffman David M Blei Francis Bach
We do not optimize these hyperparameters or vary them over time . The high Dirichlet priors are chosen to prevent sparsity in topic distributions , while the other parameters are selected as the best from Hoffman et al. ( 2010 ) . In order to optimize the number of topics K , we run five trials of each modality for 2000 iterations for K = 150 , 100 , 150 , 200 , 2501 ( a total of 25 runs per setup ) .
The high Dirichlet priors are chosen to prevent sparsity in topic distributions , while the other parameters are selected as the best from @@CITATION .