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Guoqiang Li edited comment on SPARK-1405 at 1/30/15 8:34 AM: ------------------------------------------------------------- Here is a sampling faster branch(work in progress): https://github.com/witgo/spark/tree/lda_MH [It's|https://github.com/witgo/spark/tree/lda_MH] computational complexity is O(log(K)) K is the number of topic [#2388|https://github.com/apache/spark/pull/2388]'s computational complexity is O(log(K)+ Nkd) , K is the number of topic and Ndk is the number of tokens in document d that are assigned to topic k was (Author: gq): Here is a sampling faster branch(work in progress): https://github.com/witgo/spark/tree/lda_MH [It's|https://github.com/witgo/spark/tree/lda_MH] computational complexity is O(log(K)) K is the number of topic [#2388|https://github.com/apache/spark/pull/2388]'s computational complexity is O(log(K)) + Nkd, K is the number of topic and Ndk is the number of tokens in document d that are assigned to topic k > 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: Joseph K. Bradley > 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