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From "Stavros Kontopoulos (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-5588) Add a unit scaler based on different norms
Date Tue, 14 Feb 2017 21:08:42 GMT

    [ https://issues.apache.org/jira/browse/FLINK-5588?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15866685#comment-15866685

Stavros Kontopoulos commented on FLINK-5588:

[~till.rohrmann] Have already implemented the Normalizer... need to check floating arithmetic
for the UnitScaler because the sum might lead to overflow:
Reference: http://www.scan2014.uni-wuerzburg.de/fileadmin/10030000/scan2014/talks/B2_2.pdf...
Standard scaler uses this algo: http://www.cs.yale.edu/publications/techreports/tr222.pdf
I am ok with norms 1,2 but i am not sure about p>2

> 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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