featurewidth

Featurewidth is a term used in data science and related fields to describe the effective width or span of a feature's influence within a representation or measurement. It captures the scale over which a feature is defined or remains informative, and can refer to temporal, spatial, or spectral extent depending on context.

In time-series analysis, featurewidth often corresponds to the window length used to compute a feature, such as moving-window statistics or spectral features. In computer vision and image processing, it may denote the size of the region over which a feature responds, for example the receptive field or the scale of blob detectors. In statistical modeling, featurewidth can indicate the range of input values or the support of a feature’s nonzero values; features with wider widths can capture longer-range patterns but may be less localized.

Measurement and selection: Featurewidth is usually expressed as a length or scale parameter (such as samples, pixels, seconds, or frequency bandwidth). It is chosen based on domain knowledge or treated as a hyperparameter to optimize; larger widths emphasize broader context, smaller widths emphasize locality. The appropriate width often involves a trade-off between context and precision, as well as between robustness to noise and the ability to resolve fine structure.

Implications: The choice of featurewidth influences model bias and variance and affects interpretability, redundancy, and the ability to distinguish patterns at different scales. It is commonly tuned together with other feature extraction parameters to align with the goals of a given analysis.

See also: feature extraction, scale, receptive field, window size, bandwidth.