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Posted to issues@spark.apache.org by "Christos Iraklis Tsatsoulis (JIRA)" <ji...@apache.org> on 2015/11/05 19:56:27 UTC
[jira] [Updated] (SPARK-11530) Return eigenvalues with PCA model
[ https://issues.apache.org/jira/browse/SPARK-11530?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Christos Iraklis Tsatsoulis updated SPARK-11530:
------------------------------------------------
Description:
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.
See this SO question http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/
and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/
for details.
was:
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.
See this SO question http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/)
and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/
for details.
> Return eigenvalues with PCA model
> ---------------------------------
>
> Key: SPARK-11530
> URL: https://issues.apache.org/jira/browse/SPARK-11530
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 1.5.1
> Reporter: Christos Iraklis Tsatsoulis
>
> 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.
> See this SO question http://stackoverflow.com/questions/33428589/pyspark-and-pca-how-can-i-extract-the-eigenvectors-of-this-pca-how-can-i-calcu/
> and this blog post http://www.nodalpoint.com/pca-in-spark-1-5/
> for details.
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