Hi Lorenz, I'm not aware of people working on hierarchical topic models for MLlib, but that would be cool to see. Hopefully other devs know more!
Glad that the current LDA is helpful! Joseph On Wed, Jun 3, 2015 at 6:43 AM, Lorenz Fischer <lorenz.fisc...@gmail.com> wrote: > Hi All > > I'm working on a project in which I use the current LDA implementation > that has been contributed by Databricks' Joseph Bradley et al. for the > recent 1.3.0 release (thanks guys!). While this is great, my project > requires several levels of topics, as I would like to offer users to drill > down into subtopics. > > As I understand it, Hierarchical Latent Dirichlet Allocation (HLDA) would > offer such a hierarchy. Looking at the papers and talks by Blei [1,2] and > Jordan [3], I think I should be able to implement HLDA in Spark using the > Nested Chinese Restaurant Process (NCRP). However, as I have some time > constraints, I'm not sure if I will have the time to do it 'the proper way'. > > In any case, I wanted to quickly ask around if anybody is already working > on this or on some other form of a hierarchical topic model. Maybe I could > contribute to these efforts instead of starting from scratch. > > Best, > Lorenz > > [1] http://www.cs.princeton.edu/~blei/papers/BleiGriffithsJordan2009.pdf > [2] > http://papers.nips.cc/paper/2466-hierarchical-topic-models-and-the-nested-chinese-restaurant-process.pdf > [3] https://www.youtube.com/watch?v=PxgW3lOrj60 >
