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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SPARK-21209) Implement Incremental PCA algorithm for ML
Date Thu, 01 Mar 2018 19:49:00 GMT

    [ https://issues.apache.org/jira/browse/SPARK-21209?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16382537#comment-16382537
] 

Apache Spark commented on SPARK-21209:
--------------------------------------

User 'sandecho' has created a pull request for this issue:
https://github.com/apache/spark/pull/20708

> 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: 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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