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Application of equality constraints on variables during alternating least squares procedures
Author(s) -
Van Benthem Mark H.,
Keenan Michael R.,
Haaland David M.
Publication year - 2002
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.761
Subject(s) - least squares function approximation , chemometrics , total least squares , mathematics , generalized least squares , non linear least squares , mathematical optimization , basis (linear algebra) , algorithm , computer science , explained sum of squares , statistics , regression , geometry , machine learning , estimator
We describe several methods of applying equality constraints while performing procedures that employ alternating least squares. Among these are mathematically rigorous methods of applying equality constraints, as well as approximate methods, commonly used in chemometrics, that are not mathematically rigorous. The rigorous methods are extensions of the methods described in detail in Lawson and Hanson's landmark text on solving least squares problems, which exhibit well‐behaved least squares performance. The approximate methods tend to be easy to use and code, but they exhibit poor least squares behaviors and have properties that are not well understood. This paper explains the application of rigorous equality‐constrained least squares and demonstrates the dangers of employing non‐rigorous methods. We found that in some cases, upon initiating multivariate curve resolution with the exact basis vectors underlying synthetic data overlaid with noise, the approximate method actually results in an increase in the magnitude of residuals. This phenomenon indicates that the solutions for the approximate methods may actually diverge from the least squares solution. Copyright © 2002 John Wiley & Sons, Ltd.