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Date: Wed, 26 Oct 2011 17:47:34 +0000 (UTC)
From: "Olivier Grisel (Commented) (JIRA)" <jira@apache.org>
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Subject: [jira] [Commented] (MAHOUT-836) On donating my Robust PCA Java code
 to Mahout
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    [ https://issues.apache.org/jira/browse/MAHOUT-836?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13136150#comment-13136150 ] 

Olivier Grisel commented on MAHOUT-836:
---------------------------------------

As explained in Sujit's blog post, RPCA is minimizing an objective function that combines the nuclear norm of a dense low-rank approximation of the data + the L_1 norm of a sparse additive noise matrix.

As a complement to this blog post, here is a very interesting tutorial by Emmanuel Candes here (the RPCA part starts around the middle of the presentation, min 38):

  http://videolectures.net/mlss2011_candes_lowrank/
                
> On donating my Robust PCA Java code to Mahout
> ---------------------------------------------
>
>                 Key: MAHOUT-836
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-836
>             Project: Mahout
>          Issue Type: New JIRA Project
>          Components: Classification
>         Environment: Platform independent
>            Reporter: Sujit Nair
>              Labels: newbie
>   Original Estimate: 672h
>  Remaining Estimate: 672h
>
> Hi All,
> I have an implementation of Robust PCA (a.k.a low rank and sparse decomposition) in Java which I would like to donate to Mahout. I am a MATLAB expert, comfortable with C++ and have just started with Java. I am completely new to Mahout but am very excited to participate and contribute. 
> I have tested my code exhaustively and there does not seem to be any issues. The results are very good but the code definitely needs some optimization. 
> Please let me know if there is interest. 
> Thanks,
> Sujit

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