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https://issues.apache.org/jira/browse/SPARK-7514?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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yuhao yang updated SPARK-7514:
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Description:
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
was:
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 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
> 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
> 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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