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https://issues.apache.org/jira/browse/FLINK-5588?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15867964#comment-15867964
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Stavros Kontopoulos edited comment on FLINK-5588 at 2/15/17 2:58 PM:
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Hi [~till.rohrmann] my pleasure. I will wait for the review, meanwhile I will
continue working on the other stuff and review PRs. It is important at some
point for people involved here to discuss roadmap and plan releases of several
things. Maybe it would be good to start the discussion on the list.
was (Author: skonto):
Hi [~till.rohrmann] my pleasure. I will wait for the review, meanwhile I will
continue working on the other stuff and review PRs. It is important at some
point for people involved here to discuss roadmap and plan releases of several
things.
> Add a unit scaler based on different norms
> ------------------------------------------
>
> Key: FLINK-5588
> URL: https://issues.apache.org/jira/browse/FLINK-5588
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Stavros Kontopoulos
> Assignee: Stavros Kontopoulos
> Priority: Minor
>
> So far ML has two scalers: min-max and the standard scaler.
> A third one frequently used, is the scaler to unit.
> We could implement a transformer for this type of scaling for different norms
> available to the user.
> I will make a separate class for the Normalization per sample procedure by
> using the Transformer API because it is easy to add
> it, fit method does nothing in this case.
> Scikit-learn has also some calls available outside the Transform API, we
> might want add that in the future.
> These calls work on any axis but they are not re-usable in a pipeline [4]
> Right now the existing scalers in Flink ML support per feature normalization
> by using the Transformer API.
> Resources
> [1] https://en.wikipedia.org/wiki/Feature_scaling
> [2]
> http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.scale.html
> [3] https://spark.apache.org/docs/2.1.0/mllib-feature-extraction.html
> [4] http://scikit-learn.org/stable/modules/preprocessing.html
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