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https://issues.apache.org/jira/browse/SPARK-11530?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiangrui Meng updated SPARK-11530:
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Shepherd: Xiangrui Meng (was: Sean Owen)
Assignee: Sean Owen
> 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
>
> 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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