Normalizeade

Normalizeade is a data normalization technique used to rescale numerical features for statistical analysis and machine learning. It aims to preserve important distributional characteristics of the data while remaining robust to outliers and skew.

The method is based on adaptive distribution estimation (ADE). For each feature, normalizeade computes a robust center and scale using medians and median absolute deviations, then estimates the empirical distribution with ADE to capture skewness and tail behavior. Data are transformed by mapping the estimated quantiles to a target scale, typically a standard normal or [0,1].

Options include batch normalizeade, which operates on a full dataset, and online normalizeade, which updates statistics incrementally. The method can be combined with post-transformation skew correction, such as a Yeo-Johnson-like step, to further normalize the distribution.

Applications and considerations: Normalizeade is designed for heterogeneous data where outliers are present, helping to stabilize learning in supervised and unsupervised tasks. It is more computationally intensive than simple z-score normalization and may require careful parameterization of ADE. It can distort linear relationships if over-applied; practitioners should compare with standard methods.

See also: Z-score normalization, Min-max normalization, Robust scaling, Quantile transformer, Data preprocessing.