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https://issues.apache.org/jira/browse/FLINK-1718?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Till Rohrmann resolved FLINK-1718.
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Resolution: Fixed
Added with d2e2d79fc0052c064188940520c93bbd0c1b1d4b
> 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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