[GitHub] spark pull request: [ML][MLLIB] SPARK-2426: Integrate Breeze Quadr...

[debasish83 <gi...@git.apache.org>]

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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