Factor analysis in which factors are calculated that successively capture the greatest variance in the data set.
Notes: - The factors are orthogonal and are known as principal-component factors.
- 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.
- The term "principal-component analysis" is preferred to the plural "principal-components analysis".
See: non-linear iterative partial least squares
Source:
PAC, 2016, 88, 407. 'Vocabulary of concepts and terms in chemometrics (IUPAC Recommendations 2016)' on page 423 (https://doi.org/10.1515/pac-2015-0605)