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From "Ben St. Clair (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-21209) Implement Incremental PCA algorithm for MLlib
Date Mon, 26 Jun 2017 10:25:00 GMT

     [ https://issues.apache.org/jira/browse/SPARK-21209?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Ben St. Clair updated SPARK-21209:
----------------------------------
    Description: 
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.

  was: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.


> Implement Incremental PCA algorithm for MLlib
> ---------------------------------------------
>
>                 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
>              Labels: 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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