[jira] [Resolved] (SPARK-11530) Return eigenvalues with PCA model

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-11530?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved SPARK-11530.
-------------------------------
       Resolution: Fixed
    Fix Version/s: 2.0.0

Issue resolved by pull request 9736
[https://github.com/apache/spark/pull/9736]

> Return eigenvalues with PCA model
> ---------------------------------
>
>                 Key: SPARK-11530
>                 URL: https://issues.apache.org/jira/browse/SPARK-11530
>             Project: Spark
>          Issue Type: Improvement
>          Components: ML, MLlib
>    Affects Versions: 1.5.1
>            Reporter: Christos Iraklis Tsatsoulis
>            Assignee: Sean Owen
>             Fix For: 2.0.0
>
>
> For data scientists & statisticians, PCA is of little use if they cannot estimate the _proportion of variance explained_ by selecting _k_ principal components (see here for the math details: https://inst.eecs.berkeley.edu/~ee127a/book/login/l_sym_pca.html , section 'Explained variance'). To estimate this, one only needs the eigenvalues of the covariance matrix.
> Although the eigenvalues are currently computed during PCA model fitting, they are not _returned_; hence, as it stands now, PCA in Spark ML is of extremely limited practical use.
> For details, see these SO questions
> http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/ (pyspark)
> http://stackoverflow.com/questions/33559599/spark-pca-top-components (Scala)
> and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/



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