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https://issues.apache.org/jira/browse/SPARK-7514?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14537570#comment-14537570
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Joseph K. Bradley commented on SPARK-7514:
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Thanks for checking! I like the updated name MinMaxScaler.
For parameters, I like specifying min,max as in sklearn.
We could also provide a setter method of the form {{setCenterScale(center:
Double, scale: Double)}} which computes and sets min,max. (I'm using "center"
instead of "translation" since that seems more natural to me.) But that may be
superfluous, so let me know if that sounds useful.
> Add MinMaxScaler to feature transformation
> ------------------------------------------
>
> Key: SPARK-7514
> URL: https://issues.apache.org/jira/browse/SPARK-7514
> Project: Spark
> Issue Type: New Feature
> Components: MLlib
> Reporter: yuhao yang
> Original Estimate: 24h
> Remaining Estimate: 24h
>
> Add a popular scaling method to feature component, which is commonly known as
> min-max normalization or Rescaling.
> Core function is,
> Normalized( x ) = (x - min) / (max - min) * scale + newBase
> where newBase and scale are parameters of the VectorTransformer. newBase is
> the new minimum number for the feature, and scale controls the range after
> transformation. This is a little complicated than the basic MinMax
> normalization, yet it provides flexibility so that users can control the
> range more specifically. like [0.1, 0.9] in some NN application.
> for case that max == min, 0.5 is used as the raw value.
> reference:
> http://en.wikipedia.org/wiki/Feature_scaling
> http://stn.spotfire.com/spotfire_client_help/index.htm#norm/norm_scale_between_0_and_1.htm
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