[jira] [Commented] (SPARK-2199) Distributed probabilistic latent semantic analysis in MLlib

["Valeriy Avanesov (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-2199?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14037659#comment-14037659 ] 

Valeriy Avanesov commented on SPARK-2199:
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Here is the implementation we currently have. https://github.com/akopich/dplsa
Robust and non robust PLSA are implemented but no regularizers are currently supported. 

> Distributed probabilistic latent semantic analysis in MLlib
> -----------------------------------------------------------
>
>                 Key: SPARK-2199
>                 URL: https://issues.apache.org/jira/browse/SPARK-2199
>             Project: Spark
>          Issue Type: Improvement
>          Components: MLlib
>    Affects Versions: 1.1.0
>            Reporter: Denis Turdakov
>              Labels: features
>
> Probabilistic latent semantic analysis (PLSA) is a topic model which extracts topics from text corpus. PLSA was historically a predecessor of LDA. However recent research shows that modifications of PLSA sometimes performs better then LDA[1]. Furthermore, the most recent paper by same authors shows that there is a clear way to extend PLSA to LDA and beyond[2].
> We should implement distributed versions of PLSA. In addition it should be possible  to easily add user defined regularizers or combination of them. We will implement regularizers that allows
> * extract sparse topics
> * extract human interpretable topics 
> * perform semi-supervised training 
> * sort out non-topic specific terms. 
> [1] Potapenko, K. Vorontsov. 2013. Robust PLSA performs better than LDA. In Proceedings of ECIR'13.
> [2] Vorontsov, Potapenko. Tutorial on Probabilistic Topic Modeling: Additive Regularization for Stochastic Matrix Factorization. http://www.machinelearning.ru/wiki/images/1/1f/Voron14aist.pdf 



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