[jira] [Closed] (FLINK-5588) Add a unit scaler based on different norms

["Chesnay Schepler (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/FLINK-5588?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Chesnay Schepler closed FLINK-5588.
-----------------------------------
    Resolution: Won't Do

Closing since flink-ml is effectively frozen.

> 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: Library / Machine Learning
>            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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