HellingerHistogramNormalization |
Normalize histograms by scaling them to unit absolute sum, then taking the
square root of the absolute value in each attribute, times the normalization
constant \(1/\sqrt{2}\).
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InstanceLogRankNormalization |
Normalize vectors such that the smallest value of each instance is 0, the
largest is 1, but using \( \log_2(1+x) \).
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InstanceMeanVarianceNormalization |
Normalize vectors such that they have zero mean and unit variance.
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InstanceMinMaxNormalization |
Normalize vectors with respect to a given minimum and maximum in each
dimension.
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InstanceRankNormalization |
Normalize vectors such that the smallest value of each instance is 0, the
largest is 1.
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LengthNormalization |
Class to perform a normalization on vectors to norm 1.
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Log1PlusNormalization |
Normalize the data set by applying \( \frac{\log(1+|x|b)}{\log 1+b} \) to any
value.
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