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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 04:04:44 UTC
[jira] [Updated] (SPARK-21209) Implement Incremental PCA algorithm
for ML
[ https://issues.apache.org/jira/browse/SPARK-21209?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon updated SPARK-21209:
---------------------------------
Labels: bulk-closed features (was: features)
> Implement Incremental PCA algorithm for ML
> ------------------------------------------
>
> Key: SPARK-21209
> URL: https://issues.apache.org/jira/browse/SPARK-21209
> Project: Spark
> Issue Type: New Feature
> Components: ML
> Affects Versions: 2.1.1
> Reporter: Ben St. Clair
> Priority: Major
> Labels: bulk-closed, features
>
> Incremental Principal Component Analysis is a method for calculating PCAs in an incremental fashion, allowing one to update an existing PCA model as new evidence arrives. Furthermore, an alpha parameter can be used to enable task-specific weighting of new and old evidence.
> This algorithm would be useful for streaming applications, where a fast and adaptive feature subspace calculation could be applied. Furthermore, it can be applied to combine PCAs from subcomponents of large datasets.
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