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https://issues.apache.org/jira/browse/FLINK-5588?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Stavros Kontopoulos updated FLINK-5588:
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Description:
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.
Axis for scaling either features or samples (0 for columns-features 1 for
samples-rows).
Right now the existing scalers support per feature normalization. I think its
trivial to add per sample normalization.
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
was:
So far ML has two scalers: min-max and the standard.
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.
Axis for scaling either features or samples (0 for columns-features 1 for
samples-rows).
Right now the existing scalers support per feature normalization. I think its
trivial to add per sample normalization.
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
> 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.
> Axis for scaling either features or samples (0 for columns-features 1 for
> samples-rows).
> Right now the existing scalers support per feature normalization. I think its
> trivial to add per sample normalization.
> 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
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