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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
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Olivier Grisel commented on MAHOUT-836:
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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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