title: Eigenvectors of optimal color spectra.
abstract: Principal component analysis (PCA) and weighted PCA were applied to spectra of optimal colors belonging to the outer surface of the object-color solid or to so-called MacAdam limits. The correlation matrix formed from this data is a circulant matrix whose biggest eigenvalue is simple and the corresponding eigenvector is constant. All other eigenvalues are double, and the eigenvectors can be expressed with trigonometric functions. Found trigonometric functions can be used as a general basis to reconstruct all possible smooth reflectance spectra. When the spectral data are weighted with an appropriate weight function, the essential part of the color information is compressed to the first three components and the shapes of the first three eigenvectors correspond to one achromatic response function and to two chromatic response functions, the latter corresponding approximately to Munsell opponent-hue directions 9YR-9B and 2BG-2R.
authors: Mika
authors: M
authors: Flinkman
authors: Hannu
authors: H
authors: Laamanen
authors: Jukka
authors: J
authors: Tuomela
authors: Pasi
authors: P
authors: Vahimaa
authors: Markku
authors: M
authors: Hauta-Kasari
mesh_terms: D000465:Algorithms; D003116:Color; D003118:Color Perception / Q000502:physiology*; D055253:Color Vision / Q000502:physiology*; D005583:Fourier Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008962:Models, Theoretical; D025341:Principal Component Analysis; D012984:Software; D014785:Vision, Ocular / Q000502:physiology*