non-linear iterative partial least squares

initialism: NIPALS
https://doi.org/10.1351/goldbook.10101

Iterative decomposition of a data matrix to give principal components.

Notes:
  1. Writing the model as \(\boldsymbol{X} = \boldsymbol{T}\boldsymbol{P}^{\rm{T}} + \boldsymbol{E}\), the first principal component is computed from a data matrix. The data explained by this PC are then subtracted from \(\boldsymbol{X}\) and the algorithm applied again to residual data. The procedure is repeated until sufficient principal components are obtained.
  2. The algorithm is very fast if only a few principal components are required, because the covariance matrix is not computed.
Source:
PAC, 2016, 88, 407. 'Vocabulary of concepts and terms in chemometrics (IUPAC Recommendations 2016)' on page 422 (https://doi.org/10.1515/pac-2015-0605)