Christos Iraklis Tsatsoulis created SPARK-11530: ---------------------------------------------------
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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org