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From jkbradley <>
Subject [GitHub] spark pull request: [SPARK-1405] [mllib] Latent Dirichlet Allocati...
Date Thu, 15 Jan 2015 19:34:27 GMT
Github user jkbradley commented on the pull request:
    @EntilZha  Here’s a sketch of my plan.
    * UCI ML Repository data (also used by Asuncion et al., 2009):
      * KOS
      * NIPS
      * NYTimes
      * PubMed (full)
    * Wikipedia?
    Data preparation:
    * Converting to bags of words:
      * UCI datasets are given as word counts already.
      * Wikipedia dump is text.
        * I use the SimpleTokenizer in the LDAExample, which sets term = word and only accepts
alphabetic characters.
        * Use stopwords from @dlwh located at []
        * No stemming
    * Choosing vocab: For various vocabSize settings, I took the most common vocabSize terms.
    Scaling tests: *(doing these first)*
    * corpus size
    * vocabSize
    * k
    * numIterations
    Accuracy tests: *(doing these second)*
    * train on full datasets
    * Tune hyperparameters via grid search, following Asuncion et al. (2009) section 4.1.
    * Can hopefully compare with their results in Fig. 5.
    These tests will run on a 16-node EC2 cluster of r3.2xlarge instances.

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