[jira] [Created] (SPARK-7514) Add MinMaxNormalizer to feature transformation

["yuhao yang (JIRA)" <ji...@apache.org>]

yuhao yang created SPARK-7514:
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             Summary: Add MinMaxNormalizer 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


Add a new 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 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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