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

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-2199?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved SPARK-2199.
------------------------------
    Resolution: Won't Fix

> Distributed probabilistic latent semantic analysis in MLlib
> -----------------------------------------------------------
>
>                 Key: SPARK-2199
>                 URL: https://issues.apache.org/jira/browse/SPARK-2199
>             Project: Spark
>          Issue Type: New Feature
>          Components: MLlib
>            Reporter: Denis Turdakov
>            Assignee: Valeriy Avanesov
>              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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