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https://issues.apache.org/jira/browse/MADLIB-948?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15163525#comment-15163525
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ASF GitHub Bot commented on MADLIB-948:
---------------------------------------
GitHub user orhankislal opened a pull request:
https://github.com/apache/incubator-madlib/pull/24
PCA: Proportion of variance for PCA training function
JIRA: MADLIB-948
Minor fixes:
-Added online help for pca_train and pca_sparse_train
-Unified error messages for clarity
-Fixed bug with a variance border case(1.0)
-Fixed docs to reflect correct mean table/column name
-Fixed docs to reflect the allowed ranges for components_param
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/orhankislal/incubator-madlib func/pca_prop
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/incubator-madlib/pull/24.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 #24
----
commit ba7db1c5fa70a9b5ffd06e178cb892f907c85d77
Author: Orhan Kislal <[email protected]>
Date: 2016-02-24T00:26:25Z
PCA: Proportion of variance for PCA training function
JIRA: MADLIB-948
Minor fixes:
-Added online help for pca_train and pca_sparse_train
-Unified error messages for clarity
-Fixed bug with a variance border case(1.0)
-Fixed docs to reflect correct mean table/column name
-Fixed docs to reflect the allowed ranges for components_param
----
> 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.
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