Data pre-processing in which the mean value of a variable is subtracted from data across all objects. \[a_{i,j}^{*} = a_{i,j} - \frac{1}{n}\!\sum\limits_{i\,=\,1}^{n} a_{i,j}\]
Notes: - Mean centering emphasises the differences between samples rather than differences between the samples and the variable’s origin (zero).
- Mean centering is generally recommended for principal component analysis, partial least squares and discriminant analysis of data, where relative values across the samples are more important than their absolute deviation from zero. Mean centering is not compatible with non-negativity constraints in, for example, multivariate curve resolution.
- Mean centering is generally applied with other data pre-processing methods.
See: Scaling
Source:
PAC, 2016, 88, 407. 'Vocabulary of concepts and terms in chemometrics (IUPAC Recommendations 2016)' on page 410 (https://doi.org/10.1515/pac-2015-0605)