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https://issues.apache.org/jira/browse/FLINK-3128?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15130415#comment-15130415
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ASF GitHub Bot commented on FLINK-3128:
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Github user f-sander commented on the pull request:
https://github.com/apache/flink/pull/1565#issuecomment-179247125
There is one advantage of this over using a single-node ML-Lib: This
implementation contains the compression procedure used in Spark that combines
data points with equal label. The hope of this parallelization strategy is,
that in each partition enough points are compressed so that the combined
dataset in the last step fits into one node.
I will try to outline our algorithm tonight, but I'm very busy right now
and can't promise. But I'll try.
> Add Isotonic Regression To ML Library
> -------------------------------------
>
> Key: FLINK-3128
> URL: https://issues.apache.org/jira/browse/FLINK-3128
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Fridtjof Sander
> Assignee: Fridtjof Sander
> Priority: Minor
>
> Isotonic Regression fits a monotonically increasing function (also called
> isotonic function) to a plane of datapoints.
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