Hi, Can anyone please shed more light on the PCA implementation in spark? The documentation is a bit leaving as I am not sure I understand the output. According to the docs, the output is a local matrix with the columns as principal components and columns sorted in descending order of covariance. This is a bit confusing for me as I need to compute other statistic Like standard deviation of the principal components. How do I match the principal components to the actual features since there is some sorting? How about eigenvectors and eigenvalues?
Please anyone to help shed light on the output, how to use it further and pca spark implementation in general is appreciated Thank you in earnest -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/spark1-0-principal-component-analysis-tp9249.html Sent from the Apache Spark User List mailing list archive at Nabble.com.
