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Joseph K. Bradley updated SPARK-5563: ------------------------------------- Issue Type: Improvement (was: Test) > LDA with online variational inference > ------------------------------------- > > Key: SPARK-5563 > URL: https://issues.apache.org/jira/browse/SPARK-5563 > Project: Spark > Issue Type: Improvement > Components: MLlib > Affects Versions: 1.3.0 > Reporter: Joseph K. Bradley > > Latent Dirichlet Allocation (LDA) parameters can be inferred using online > variational inference, as in Hoffman, Blei and Bach. “Online Learning for > Latent Dirichlet Allocation.” NIPS, 2010. This algorithm should be very > efficient and should be able to handle much larger datasets than batch > algorithms for LDA. > This algorithm will also be important for supporting Streaming versions of > LDA. > The implementation will ideally use the same API as the existing LDA but use > a different underlying optimizer. > This will require hooking in to the existing mllib.optimization frameworks. > This will require some discussion about whether batch versions of online > variational inference should be supported, as well as what variational > approximation should be used now or in the future. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org