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:
- 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".