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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-1718) Add sparse vector and sparse matrix types to machine learning library
Date Wed, 01 Apr 2015 09:35:53 GMT

    [ https://issues.apache.org/jira/browse/FLINK-1718?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14390282#comment-14390282
] 

ASF GitHub Bot commented on FLINK-1718:
---------------------------------------

Github user tillrohrmann commented on the pull request:

    https://github.com/apache/flink/pull/539#issuecomment-88414948
  
    FYI: Travis failed only for the last profile because the tests didn't start for some reason.
Travis passed for my own repository, though.


> Add sparse vector and sparse matrix types to machine learning library
> ---------------------------------------------------------------------
>
>                 Key: FLINK-1718
>                 URL: https://issues.apache.org/jira/browse/FLINK-1718
>             Project: Flink
>          Issue Type: New Feature
>          Components: Machine Learning Library
>            Reporter: Till Rohrmann
>            Assignee: Till Rohrmann
>              Labels: ML
>
> Currently, the machine learning library only supports dense matrix and dense vectors.
For future algorithms it would be beneficial to also support sparse vectors and matrices.
> I'd propose to use the compressed sparse column (CSC) representation, because it allows
rather efficient operations compared to a map backed sparse matrix/vector implementation.
Furthermore, this is also the format the Breeze library expects for sparse matrices/vectors.
Thus, it is easy to convert to a sparse breeze data structure which provides us with many
linear algebra operations.
> BIDMat [1] uses the same data representation.
> Resources:
> [1] [https://github.com/BIDData/BIDMat]



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