GitHub user rezazadeh opened a pull request: https://github.com/apache/spark/pull/88
Sparkpca # Principal Component Analysis Computes the top k principal component coefficients for the m-by-n data matrix X. Rows of X correspond to observations and columns correspond to variables. The coefficient matrix is n-by-k. Each column of the coefficients return matrix contains coefficients for one principal component, and the columns are in descending order of component variance. This function centers the data and uses the singular value decomposition (SVD) algorithm. ## Testing Tests included: * All principal components * Only top k principal components The results are tested against MATLAB's pca: http://www.mathworks.com/help/stats/pca.html ## Documentation Added to mllib-guide.md ## Example Usage Added to examples directory under SparkPCA.scala You can merge this pull request into a Git repository by running: $ git pull https://github.com/rezazadeh/spark sparkpca Alternatively you can review and apply these changes as the patch at: https://github.com/apache/spark/pull/88.patch To close this pull request, make a commit to your master/trunk branch with (at least) the following in the commit message: This closes #88 ---- commit 78738a9de0d99df3b2cb8966172ef2e09277a156 Author: Reza Zadeh <riz...@gmail.com> Date: 2014-03-06T03:24:44Z initial files commit 1dfd2cf27a420dfb265ca8de0368286bc23c0b83 Author: Reza Zadeh <riz...@gmail.com> Date: 2014-03-06T03:26:53Z all files from old pr commit 1841d78710c88e8eed3a3bdb3c2b7fff2ee678f0 Author: Reza Zadeh <riz...@gmail.com> Date: 2014-03-06T03:30:52Z bad chnage undo ---- --- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. ---