[jira] [Updated] (SPARK-7514) Add MinMaxNormalizer to feature transformation
[ https://issues.apache.org/jira/browse/SPARK-7514?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] yuhao yang updated SPARK-7514: -- 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 -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-7514) Add MinMaxNormalizer to feature transformation
[ https://issues.apache.org/jira/browse/SPARK-7514?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] yuhao yang updated SPARK-7514: -- Description: 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 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 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 -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Updated] (SPARK-7514) Add MinMaxNormalizer to feature transformation
[ https://issues.apache.org/jira/browse/SPARK-7514?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ] yuhao yang updated SPARK-7514: -- Description: 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 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 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 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 -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
