After reading Hanna Wallach's paper Rethinking LDA: Why Priors Matter, I want to add hyper-parameter optimization to my own implementation of LDA. However, the paper doesn't given any details about how this optimization is to be done. I suppose I could dive into MALLET source code, but I want to understand this to the point that I can implement it rather than just copy code. Does anyone have any pointers to papers or tutorials that can help me?
Edit: Apparently there is a comparison of 5 different methods for optimizing the hyper-parameters in a Dirichlet-multinomial context in Wallach's PhD disertation. I found this on the topic models mailing list at Princeton, but I also missed the reference to the dissertation in the priors paper. I'm going to read through the disertation and then see if I can distill the ideas specifically for LDA in an answer to my own question, but I would be glad to accept someone else's answer if they beat me to it :)