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Valeriy Avanesov commented on SPARK-5564: ----------------------------------------- I am considering working on this issue. The question is whether there should be another EMLDAOptimizerVorontsov or shall the existing EMLDAOptimizer be re-written. > Support sparse LDA solutions > ---------------------------- > > Key: SPARK-5564 > URL: https://issues.apache.org/jira/browse/SPARK-5564 > Project: Spark > Issue Type: Improvement > Components: MLlib > Affects Versions: 1.3.0 > Reporter: Joseph K. Bradley > > Latent Dirichlet Allocation (LDA) currently requires that the priors’ > concentration parameters be > 1.0. It should support values > 0.0, which > should encourage sparser topics (phi) and document-topic distributions > (theta). > For EM, this will require adding a projection to the M-step, as in: Vorontsov > and Potapenko. "Tutorial on Probabilistic Topic Modeling : Additive > Regularization for Stochastic Matrix Factorization." 2014. -- This message was sent by Atlassian JIRA (v6.4.14#64029) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org