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Posted to issues@spark.apache.org by "zhengruifeng (Jira)" <ji...@apache.org> on 2019/12/04 01:51:00 UTC
[jira] [Resolved] (SPARK-30109) PCA use BLAS.gemv with sparse
vector
[ https://issues.apache.org/jira/browse/SPARK-30109?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
zhengruifeng resolved SPARK-30109.
----------------------------------
Fix Version/s: 3.0.0
Resolution: Fixed
Issue resolved by pull request 26745
[https://github.com/apache/spark/pull/26745]
> PCA use BLAS.gemv with sparse vector
> ------------------------------------
>
> Key: SPARK-30109
> URL: https://issues.apache.org/jira/browse/SPARK-30109
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 3.0.0
> Reporter: zhengruifeng
> Assignee: zhengruifeng
> Priority: Minor
> Fix For: 3.0.0
>
>
> When PCA was first impled in [SPARK-5521|https://issues.apache.org/jira/browse/SPARK-5521], at that time Matrix.multiply(BLAS.gemv internally) did not support sparse vector. So it worked around it by applying a complex matrix multiplication.
> Since [SPARK-7681|https://issues.apache.org/jira/browse/SPARK-7681], BLAS.gemv supported sparse vector. So we can directly use Matrix.multiply now.
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