Github user debasish83 commented on the pull request:
https://github.com/apache/spark/pull/3221#issuecomment-90753041
@mengxr @josephk In my internal testing, I am finding the sparse
formulations useful for extracting genre/topic information out of
netflix/movielens dataset...The formulations are:
1. Sparse coding: L2 on users/words, L1 on documents/movies
2. L2 on users/words, probability simplex on documents/movies
The reference:
2011 Sparse Latent Semantic Analysis LSA(some of it is implemented in
Graphlab):
https://www.cs.cmu.edu/~xichen/images/SLSA-sdm11-final.pdf
showed sparse coding producing better result than LDA...I am considering if
it makes sense to add a 20 newsgroup flow in examples that was shown in the
paper ? Also do we have perplexity implemented so that we can start comparing
topic models...The ALS runtime with sparse formulations are also pretty good....
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