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Debasish Das commented on SPARK-1405: ------------------------------------- NIPS dataset is common for PLSA and additive regularization based matrix factorization formulations as well since the experiments in this paper focused on the NIPS dataset as well... http://www.machinelearning.ru/wiki/images/1/1f/Voron14aist.pdf I will be using NIPS dataset for quality experiments but for scaling experiments, wiki data is good...wiki data was demo-ed by Databricks in last spark summit...it will be great if we can get it from that demo > parallel Latent Dirichlet Allocation (LDA) atop of spark in MLlib > ----------------------------------------------------------------- > > Key: SPARK-1405 > URL: https://issues.apache.org/jira/browse/SPARK-1405 > Project: Spark > Issue Type: New Feature > Components: MLlib > Reporter: Xusen Yin > Assignee: Guoqiang Li > Priority: Critical > Labels: features > Attachments: performance_comparison.png > > Original Estimate: 336h > Remaining Estimate: 336h > > Latent Dirichlet Allocation (a.k.a. LDA) is a topic model which extracts > topics from text corpus. Different with current machine learning algorithms > in MLlib, instead of using optimization algorithms such as gradient desent, > LDA uses expectation algorithms such as Gibbs sampling. > In this PR, I prepare a LDA implementation based on Gibbs sampling, with a > wholeTextFiles API (solved yet), a word segmentation (import from Lucene), > and a Gibbs sampling core. -- 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