normaliin
Normaliin is a theoretical construct used in discussions of data normalization and feature scaling. It denotes a family of normalization operators intended to adjust data magnitudes while preserving selected geometric or statistical properties. The term appears mainly in speculative or pedagogical contexts and does not correspond to a single universally adopted algorithm.
A common interpretation defines normaliin as a map that takes a data vector x in a real
Properties commonly associated with normaliin include context sensitivity (scaling reflects the chosen metric M), compatibility with
Example: for x = (2, -3, 4) and M = diag(1, 2, 1) with t = 0, g = sqrt(38) ≈
Applications of normaliin are discussed in machine learning preprocessing, robust statistics, and data fusion. Limitations include