[jira] [Updated] (SPARK-7514) Add MinMaxScaler to feature transformation

["Joseph K. Bradley (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-7514?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Joseph K. Bradley updated SPARK-7514:
-------------------------------------
    Assignee: yuhao yang

> 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
>            Assignee: 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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