Christos Iraklis Tsatsoulis created SPARK-11530:
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Summary: 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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