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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