https://doi.org/10.1351/goldbook.10101
Iterative decomposition of a data matrix to give principal components.
Notes:
- 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.
- The algorithm is very fast if only a few principal components are required, because the covariance matrix is not computed.