Frank McQuillan created MADLIB-948:
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Summary: 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
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.
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