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Debasish Das edited comment on SPARK-5564 at 3/30/15 12:30 AM: --------------------------------------------------------------- [~josephkb] could you please point me to the datasets that are used for benchmarking? I have started testing loglikelihood loss for recommendation and since I already added the constraints, this is the right time to test it on LDA benchmarks as well...I will open up the code as part of https://issues.apache.org/jira/browse/SPARK-6323 as soon as our legal clears it... I am looking into LDA test-cases but since I am optimizing log-likelihood directly, I am looking to add more testcases based on document and word matrix...For recommendation, I know how to construct the testcases with loglikelihood loss.... was (Author: debasish83): [~josephkb] could you please point me to the datasets that are used for benchmarking? I have started testing loglikelihood loss for recommendation and since I already added the constraints, this is the right time to test it on LDA benchmarks as well...I will open up the code as part of https://issues.apache.org/jira/browse/SPARK-6323 as soon as our legal clears it... I am looking into LDA test-cases but since I am optimizing log-likelihood directly, I am looking to add more testcases from your LDA JIRA...For recommendation, I know how to construct the testcases... > 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 (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org