[jira] [Comment Edited] (SPARK-5563) LDA with online variational inference
[ https://issues.apache.org/jira/browse/SPARK-5563?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14364350#comment-14364350 ] yuhao yang edited comment on SPARK-5563 at 3/17/15 1:13 AM: Matthew Willson. Thanks for the attention and idea. Apart from Gensim, vowpal-wabbit also has a distributed implementation (C++) provided by Matthew D. Hoffman, which seems to be amazingly fast. I'll refer to those libraries as much as possible. And suggestions are always welcome. was (Author: yuhaoyan): Matthew Willson. Thanks for the attention and idea. Apart from Gensim, vowpal-wabbit also has a distributed implementation provided by Matthew D. Hoffman, which seems to be amazingly fast. I'll refer to those libraries as much as possible. And suggestions are always welcome. > 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 >Assignee: yuhao yang > > 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
[jira] [Comment Edited] (SPARK-5563) LDA with online variational inference
[ https://issues.apache.org/jira/browse/SPARK-5563?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14305115#comment-14305115 ] yuhao yang edited comment on SPARK-5563 at 2/4/15 2:23 PM: --- Thanks Joseph for helping create the jira. Paste previous [comment link|https://issues.apache.org/jira/browse/SPARK-1405?focusedCommentId=14302952&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-14302952] here and share the current implementation at https://github.com/hhbyyh/OnlineLDA_Spark. I agree with the suggestion listed above and will propose a PR for more detailed discussion soon (ETA tomorrow). Thanks. was (Author: yuhaoyan): Thanks Joseph for helping create the jira. Paste previous [comment link|https://issues.apache.org/jira/browse/SPARK-1405?focusedCommentId=14302952&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-14302952] here and share the current implementation at https://github.com/hhbyyh/OnlineLDA_Spark. I agree with the suggestion listed above and will propose a PR for more detailed discussion soon. Thanks. > 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
[jira] [Comment Edited] (SPARK-5563) LDA with online variational inference
[ https://issues.apache.org/jira/browse/SPARK-5563?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14305115#comment-14305115 ] yuhao yang edited comment on SPARK-5563 at 2/4/15 2:22 PM: --- Thanks Joseph for helping create the jira. Paste previous [comment link|https://issues.apache.org/jira/browse/SPARK-1405?focusedCommentId=14302952&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-14302952] here and share the current implementation at https://github.com/hhbyyh/OnlineLDA_Spark. I agree with the suggestion listed above and will propose a PR for more detailed discussion soon. Thanks. was (Author: yuhaoyan): Thanks Joseph for helping create the jira. Paste previous [comment link|https://issues.apache.org/jira/browse/SPARK-1405?focusedCommentId=14302952&page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel#comment-14302952] here and share the current implementation at https://github.com/hhbyyh/OnlineLDA_Spark. I agree with the suggestion listed above and will propose a PR for more detailed discussion soon. Thanks > 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
