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https://issues.apache.org/jira/browse/SPARK-21209?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16383228#comment-16383228
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Sandeep Kumar Choudhary commented on SPARK-21209:
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Hi Ben St. Clair. I have implemented Incremental PCA model from:
`D. Ross, J. Lim, R. Lin, M. Yang, Incremental Learning for Robust Visual
Tracking, International Journal of Computer Vision, Volume 77, Issue 1-3,
pp. 125-141, May 2008.`
See http://www.cs.toronto.edu/~dross/ivt/RossLimLinYang_ijcv.pdf
I would like to have discussion on this issue.
> 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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