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From "zhengruifeng (Jira)" <j...@apache.org>
Subject [jira] [Resolved] (SPARK-30109) PCA use BLAS.gemv with sparse vector
Date Wed, 04 Dec 2019 01:51:00 GMT

     [ 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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