Fundamentals of Statistics contains material of various lectures and courses of H. Lohninger on statistics, data analysis and chemometrics......click here for more. |
Home Math Background Matrices The NIPALS Algorithm | |||||||||||||||||||||||||
See also: Eigenvectors and Eigenvalues - Advanced Discussion, Singular Value Decomposition | |||||||||||||||||||||||||
The NIPALS AlgorithmThe NIPALS Algorithm ("Nonlinear Iterative vartial Least Squares") has been developed by H. Wold ( ) at first for PCA and later-on for PLS. It is the most commonly used method for calculating the principal components of a data set. It gives more numerically accurate results when compared with the SVD of the covariance matrix, but is slower to calculate. Assuming that the data to be analyzed is stored in matrix X, the steps to calculate the loadings u and scores v of the principal components are as follows:
RemarkInterestingly enough there is a mountain in Sweden which is called Nipals too [68°89'00 N, 18°30'12 E]. |
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Home Math Background Matrices The NIPALS Algorithm |