Premium
Principal components applied to modeling: Dealing with the mean vector
Author(s) -
Worthey James A.,
Brill Michael H.
Publication year - 2004
Publication title -
color research and application
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.393
H-Index - 62
eISSN - 1520-6378
pISSN - 0361-2317
DOI - 10.1002/col.20021
Subject(s) - principal component analysis , principal (computer security) , basis (linear algebra) , function (biology) , mathematics , population , pattern recognition (psychology) , computer science , artificial intelligence , geometry , evolutionary biology , biology , operating system , demography , sociology
Principal components analysis is often used to fit a population of spectral reflectances by a mean vector plus a basis‐function expansion about the mean. Certain color‐technology applications (such as color correction) are much easier if the mean is absent. If the mean of reflectance (or of another spectral function) is a linear combination of the first few principal components (such as the first three), then a linear model can fit the original data without mentioning the mean vector in the model's formulation. This idea is worked out step by step, and a realistic example is presented. © 2004 Wiley Periodicals, Inc. Col Res Appl, 29, 261–266, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20021