scalederived

Scalederived is a term used to describe items that have been produced by applying a scaling operation to a base item. The scaling can be a simple multiplication by a factor, a normalization or standardization, or a broader scale transformation that changes magnitude while preserving structural relationships. The term is informal and not tied to a single canonical procedure; its exact meaning is defined by context and may be linear or nonlinear, isotropic or anisotropic, depending on the domain.

Common uses occur in data science, computer graphics, and signal processing. In data science, scalederived features result from transforming raw features through scaling functions such as standardization (subtracting the mean and dividing by the standard deviation), min–max normalization, or logarithmic scaling, to improve comparability or model performance. In computer graphics, a scalederived model is a version of a base geometry that has been scaled in size, either uniformly or along specific axes. In signal processing, scalederived signals are scaled copies of a base signal, which can affect amplitude and energy.

Examples help clarify the concept. If x is a base feature, x' = 3x is scalederived from x. If a vector x is transformed by a diagonal scale matrix S, then x' = Sx is scalederived. Potential cautions include changes to units, interpretability, and possible distortion of relationships if scaling is applied without accounting for context.

Related terms include scaling, normalization, and transformation. Scalederived serves as a descriptive label for outcomes of scaling operations across disciplines.