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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 updated FLINK-1718:
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Description:
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]
was:
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.
> 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
> 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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