principal-component analysis

initialism: PCA
https://doi.org/10.1351/goldbook.10106

Factor analysis in which factors are calculated that successively capture the greatest variance in the data set.

Notes:
  1. The factors are orthogonal and are known as principal-component factors.
  2. The factorization is written \(\boldsymbol{X} = \boldsymbol{T}\boldsymbol{P}^{\rm{T}} + \boldsymbol{E}\), where \(\boldsymbol{T}\) is the scores matrix, \(\boldsymbol{P}\) is the loadings matrix and \(\boldsymbol{E}\) is a residual matrix.
  3. The term "principal-component analysis" is preferred to the plural "principal-components analysis".
See: Array
Source:
PAC, 2016, 88, 407. (Vocabulary of concepts and terms in chemometrics (IUPAC Recommendations 2016)) on page 423 [Terms] [Paper]