Package elki.datasource.filter.normalization.instancewise
Instancewise normalization, where each instance is normalized independently.

Class Summary Class Description HellingerHistogramNormalization<V extends NumberVector> 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 \(1/\sqrt{2}\).HellingerHistogramNormalization.Par Parameterization class.InstanceLogRankNormalization<V extends NumberVector> Normalize vectors such that the smallest value of each instance is 0, the largest is 1, but using \( \log_2(1+x) \).InstanceLogRankNormalization.Par Parameterization class.InstanceMeanVarianceNormalization<V extends NumberVector> Normalize vectors such that they have zero mean and unit variance.InstanceMeanVarianceNormalization.Par<V extends NumberVector> Parameterization class.InstanceMinMaxNormalization<V extends NumberVector> Normalize vectors with respect to a given minimum and maximum in each dimension.InstanceMinMaxNormalization.Par<V extends NumberVector> Parameterization class.InstanceRankNormalization<V extends NumberVector> Normalize vectors such that the smallest value of each instance is 0, the largest is 1.InstanceRankNormalization.Par Parameterization class.LengthNormalization<V extends NumberVector> Class to perform a normalization on vectors to norm 1.LengthNormalization.Par<V extends NumberVector> Parameterization class.Log1PlusNormalization<V extends NumberVector> Normalize the data set by applying \( \frac{\log(1+xb)}{\log 1+b} \) to any value.Log1PlusNormalization.Par<V extends NumberVector> Parameterization class.