Github user jkbradley commented on the pull request:
https://github.com/apache/spark/pull/4047#issuecomment-70146828
@witgo I agree that there are 2 different use regimes for LDA:
interpretable topics and featurization. The current implementation follows
pretty much every other graph-based implementation Iâve seen:
* 1 vertex per document + 1 vertex per term
* Each vertex stores a vector of length # topics.
* On each iteration, each doc vertex must communicate its vector to any
connected term vertices (and likewise for term vertices), via map-reduce stages
over triplets.
I have not heard of methods which can avoid this amount of communication
for LDA. Iâm sure the implementation can be optimized, so please make
comments here or JIRAs afterwards about that. For modified models, it might be
possible to communicate less: sparsity-inducing priors, hierarchical models,
etc.
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