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https://issues.apache.org/jira/browse/SPARK-7514?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14537602#comment-14537602
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yuhao yang commented on SPARK-7514:
-----------------------------------
Class name has always been MinMaxScaler in the code, yet I named jira wrongly...
For the parameters, currently the model looks like:
class MinMaxScalerModel (
+ val min: Vector,
+ val max: Vector,
+ var newBase: Double,
+ var scale: Double) extends VectorTransformer
I have used min, max to store the model statistics. In some articles, the range
bounds are named newMin / newMax (I think it can be confusing).
ran out of variable names here...
setCenterScale looks good.
> 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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