[jira] [Resolved] (MADLIB-948) Proportion of variance for PCA training function

["Frank McQuillan (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MADLIB-948?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Frank McQuillan resolved MADLIB-948.
------------------------------------
    Resolution: Fixed

> Proportion of variance for PCA training function
> ------------------------------------------------
>
>                 Key: MADLIB-948
>                 URL: https://issues.apache.org/jira/browse/MADLIB-948
>             Project: Apache MADlib
>          Issue Type: New Feature
>            Reporter: Frank McQuillan
>            Priority: Minor
>             Fix For: v2.0
>
>
> In future iterations of the pca_train command, is it feasible to insert another optional command called variance_proportion? Instead of specifying k principal components to compute, you instead specify the proportion of variance that you want your PCA vectors to account for. The number of principal vectors generated would depend the covariance matrix/correlation matrix (depending on whether you normalized or not) and variance_proportion. So if I specified that variance_proportion = .8, the algorithm would terminate after obtaining enough principal vectors so that the ratio of the sum of the eigenvalues collected thus far to the trace of the covariance matrix/correlation matrix (the sum of all of the eigenvalues of the covariance matrix/correlation matrix) is greater than or equal to .8. That is, the algorithm would terminate after collecting enough vectors to account for 80% of the total variance in the set of observations.



--
This message was sent by Atlassian JIRA
(v6.3.4#6332)