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From "Till Rohrmann (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-1750) Add canonical correlation analysis (CCA) to machine learning library
Date Thu, 19 Jan 2017 09:31:26 GMT

    [ https://issues.apache.org/jira/browse/FLINK-1750?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15829584#comment-15829584

Till Rohrmann commented on FLINK-1750:

Hi [~kateri],

great to hear that you're working on this feature :-)

There wasn't a specific use case intended to be solved by this issue. Thus, it would be great
to implement it as a general purpose method where you can enter samples of two random vectors
and then can do the dependency reduction after you've learned the covariance matrix. Maybe
you could take a look at how scikit learn does it. Usually they have a really good abstraction.

There wasn't a customer requesting this feature. I opened it because I thought it would be
a valuable transformer in your ML pipeline.

I hope this helps to answer your questions.

> Add canonical correlation analysis (CCA) to machine learning library
> --------------------------------------------------------------------
>                 Key: FLINK-1750
>                 URL: https://issues.apache.org/jira/browse/FLINK-1750
>             Project: Flink
>          Issue Type: New Feature
>          Components: Machine Learning Library
>            Reporter: Till Rohrmann
>            Assignee: Kate Eri
>              Labels: ML
> Canonical correlation analysis (CCA) [1] can be used to find correlated features between
two random variables. Moreover, CCA can be used for dimensionality reduction.
> Maybe the work of Jia Chen and Ioannis D. Schizas [2] can be adapted to realize a distributed
CCA with Flink. 
> Resources:
> [1] [http://en.wikipedia.org/wiki/Canonical_correlation]
> [2] [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6810359]

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