R. Jafari, S. H. Amirshahi, and S. A. Hosseini Ravandi, “Spectral analysis of blacks,” Color Res. Appl. 37, 176–185 (2012).

[CrossRef]

F. Agahian, S. A. Amirshahi, and S. H. Amirshahi, “Reconstruction of reflectance spectra using weighted principal component analysis,” Color Res. Appl. 33, 360–371 (2008).

[CrossRef]

T. Harifi, S. H. Amirshahi, and F. Agahian, “Recovery of reflectance spectra from colorimetric data using principal component analysis embedded regression technique,” Opt. Rev. 15, 302–308 (2008).

[CrossRef]

S. H. Amirshahi and F. Agahian, “Basis functions of the total radiance factor of fluorescent whitening agents,” Text. Res. J. 76, 192–207 (2006).

[CrossRef]

F. Ayala, J. F. Echavarri, and P. Renet, “Use of three tristimulus values from surface reflectance spectra to calculate the principal components to reconstruct these spectra by using only three eigenvectors,” J. Opt. Soc. Am. A 23, 2020–2026 (2006).

[CrossRef]

K. Ansari, S. H. Amirshahi, and S. Moradian, “Recovery of reflectance spectra from CIE tristimulus values using a progressive database selection technique,” Color Technol. 122, 128–134 (2006).

[CrossRef]

D. Y. Tzeng and R. S. Berns, “A review of principal component analysis and its applications to color technology,” Color Res. Appl. 30, 84–98 (2005).

[CrossRef]

H. S. Fairman and M. H. Brill, “The principal components of reflectance,” Color Res. Appl. 29, 104–110 (2004).

[CrossRef]

G. Kerschen and J. C. Golinval, “Feature extraction using autoassociative neural networks,” Smart Mater. Struct. 13, 211–219 (2004).

G. Ketschen and J. C. Golival, “Non-linear generalization of principal component analysis: from a global to a local approach,” J. Sound Vib. 254, 867–876 (2002).

[CrossRef]

F. Del Frate and G. Schiavon, “Non-linear principal component analysis for the radiometric inversion of atmospheric profile by using neural networks,” IEEE Trans. Geosci. Remote Sens. 37, 2335–2342 (1999).

[CrossRef]

M. A. Kramer, “Non-linear principal component analysis using autoassociative neural networks,” AIChE J. 37, 233–243 (1991).

[CrossRef]

G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Math. Control Signals Syst. 2, 303–314 (1989).

[CrossRef]

L. T. Maloney, “Evaluation of linear models of surface spectral reflectance with small number of parameters,” J. Opt. Soc. Am. 3, 29–33 (1986).

[CrossRef]

J. B. Cohen, “Dependency of the spectral reflectance curves of the Munsell color chips,” Psychon. Sci. 1, 369–370 (1964).

F. Agahian, S. A. Amirshahi, and S. H. Amirshahi, “Reconstruction of reflectance spectra using weighted principal component analysis,” Color Res. Appl. 33, 360–371 (2008).

[CrossRef]

T. Harifi, S. H. Amirshahi, and F. Agahian, “Recovery of reflectance spectra from colorimetric data using principal component analysis embedded regression technique,” Opt. Rev. 15, 302–308 (2008).

[CrossRef]

S. H. Amirshahi and F. Agahian, “Basis functions of the total radiance factor of fluorescent whitening agents,” Text. Res. J. 76, 192–207 (2006).

[CrossRef]

A. Rayat, S. H. Amirshahi, and F. Agahian, “Compression of spectral data using Box-Cox transformation,” Color Res. Appl.; doi: 10.1002/col.21771, to be published. (First published online October 11, 2012.)

F. Agahian, S. A. Amirshahi, and S. H. Amirshahi, “Reconstruction of reflectance spectra using weighted principal component analysis,” Color Res. Appl. 33, 360–371 (2008).

[CrossRef]

R. Jafari, S. H. Amirshahi, and S. A. Hosseini Ravandi, “Spectral analysis of blacks,” Color Res. Appl. 37, 176–185 (2012).

[CrossRef]

S. Peyvandi and S. H. Amirshahi, “Generalized spectral decomposition: a theory and practice to spectral reconstruction,” J. Opt. Soc. Am. A 28, 1545–1553 (2011).

[CrossRef]

F. Agahian, S. A. Amirshahi, and S. H. Amirshahi, “Reconstruction of reflectance spectra using weighted principal component analysis,” Color Res. Appl. 33, 360–371 (2008).

[CrossRef]

T. Harifi, S. H. Amirshahi, and F. Agahian, “Recovery of reflectance spectra from colorimetric data using principal component analysis embedded regression technique,” Opt. Rev. 15, 302–308 (2008).

[CrossRef]

K. Ansari, S. H. Amirshahi, and S. Moradian, “Recovery of reflectance spectra from CIE tristimulus values using a progressive database selection technique,” Color Technol. 122, 128–134 (2006).

[CrossRef]

S. H. Amirshahi and F. Agahian, “Basis functions of the total radiance factor of fluorescent whitening agents,” Text. Res. J. 76, 192–207 (2006).

[CrossRef]

S. Farajikhah, F. Madanchi, and S. H. Amirshahi, “Non-linear principal component analysis for compression of spectral data,” presented at IS2011, Ljubljana, Slovenia, 2011.

A. Rayat, S. H. Amirshahi, and F. Agahian, “Compression of spectral data using Box-Cox transformation,” Color Res. Appl.; doi: 10.1002/col.21771, to be published. (First published online October 11, 2012.)

K. Ansari, S. H. Amirshahi, and S. Moradian, “Recovery of reflectance spectra from CIE tristimulus values using a progressive database selection technique,” Color Technol. 122, 128–134 (2006).

[CrossRef]

D. Y. Tzeng and R. S. Berns, “A review of principal component analysis and its applications to color technology,” Color Res. Appl. 30, 84–98 (2005).

[CrossRef]

H. S. Fairman and M. H. Brill, “The principal components of reflectance,” Color Res. Appl. 29, 104–110 (2004).

[CrossRef]

J. B. Cohen, “Dependency of the spectral reflectance curves of the Munsell color chips,” Psychon. Sci. 1, 369–370 (1964).

G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Math. Control Signals Syst. 2, 303–314 (1989).

[CrossRef]

F. Del Frate and G. Schiavon, “Non-linear principal component analysis for the radiometric inversion of atmospheric profile by using neural networks,” IEEE Trans. Geosci. Remote Sens. 37, 2335–2342 (1999).

[CrossRef]

H. S. Fairman and M. H. Brill, “The principal components of reflectance,” Color Res. Appl. 29, 104–110 (2004).

[CrossRef]

S. Farajikhah, F. Madanchi, and S. H. Amirshahi, “Non-linear principal component analysis for compression of spectral data,” presented at IS2011, Ljubljana, Slovenia, 2011.

G. Kerschen and J. C. Golinval, “Feature extraction using autoassociative neural networks,” Smart Mater. Struct. 13, 211–219 (2004).

G. Ketschen and J. C. Golival, “Non-linear generalization of principal component analysis: from a global to a local approach,” J. Sound Vib. 254, 867–876 (2002).

[CrossRef]

T. Harifi, S. H. Amirshahi, and F. Agahian, “Recovery of reflectance spectra from colorimetric data using principal component analysis embedded regression technique,” Opt. Rev. 15, 302–308 (2008).

[CrossRef]

R. Jafari, S. H. Amirshahi, and S. A. Hosseini Ravandi, “Spectral analysis of blacks,” Color Res. Appl. 37, 176–185 (2012).

[CrossRef]

R. Jafari, S. H. Amirshahi, and S. A. Hosseini Ravandi, “Spectral analysis of blacks,” Color Res. Appl. 37, 176–185 (2012).

[CrossRef]

G. Kerschen and J. C. Golinval, “Feature extraction using autoassociative neural networks,” Smart Mater. Struct. 13, 211–219 (2004).

G. Ketschen and J. C. Golival, “Non-linear generalization of principal component analysis: from a global to a local approach,” J. Sound Vib. 254, 867–876 (2002).

[CrossRef]

M. A. Kramer, “Non-linear principal component analysis using autoassociative neural networks,” AIChE J. 37, 233–243 (1991).

[CrossRef]

U. Kruger, J. Zhang, and L. Xie, “Developments and applications of non-linear principal component analysis—a review” [Online]. Available: http://pca.narod.ru/1MainGorbanKeglWunschZin.pdf .

S. Farajikhah, F. Madanchi, and S. H. Amirshahi, “Non-linear principal component analysis for compression of spectral data,” presented at IS2011, Ljubljana, Slovenia, 2011.

L. T. Maloney, “Evaluation of linear models of surface spectral reflectance with small number of parameters,” J. Opt. Soc. Am. 3, 29–33 (1986).

[CrossRef]

K. Ansari, S. H. Amirshahi, and S. Moradian, “Recovery of reflectance spectra from CIE tristimulus values using a progressive database selection technique,” Color Technol. 122, 128–134 (2006).

[CrossRef]

A. Rayat, S. H. Amirshahi, and F. Agahian, “Compression of spectral data using Box-Cox transformation,” Color Res. Appl.; doi: 10.1002/col.21771, to be published. (First published online October 11, 2012.)

F. Del Frate and G. Schiavon, “Non-linear principal component analysis for the radiometric inversion of atmospheric profile by using neural networks,” IEEE Trans. Geosci. Remote Sens. 37, 2335–2342 (1999).

[CrossRef]

D. Y. Tzeng and R. S. Berns, “A review of principal component analysis and its applications to color technology,” Color Res. Appl. 30, 84–98 (2005).

[CrossRef]

U. Kruger, J. Zhang, and L. Xie, “Developments and applications of non-linear principal component analysis—a review” [Online]. Available: http://pca.narod.ru/1MainGorbanKeglWunschZin.pdf .

U. Kruger, J. Zhang, and L. Xie, “Developments and applications of non-linear principal component analysis—a review” [Online]. Available: http://pca.narod.ru/1MainGorbanKeglWunschZin.pdf .

M. A. Kramer, “Non-linear principal component analysis using autoassociative neural networks,” AIChE J. 37, 233–243 (1991).

[CrossRef]

D. Y. Tzeng and R. S. Berns, “A review of principal component analysis and its applications to color technology,” Color Res. Appl. 30, 84–98 (2005).

[CrossRef]

H. S. Fairman and M. H. Brill, “The principal components of reflectance,” Color Res. Appl. 29, 104–110 (2004).

[CrossRef]

F. Agahian, S. A. Amirshahi, and S. H. Amirshahi, “Reconstruction of reflectance spectra using weighted principal component analysis,” Color Res. Appl. 33, 360–371 (2008).

[CrossRef]

R. Jafari, S. H. Amirshahi, and S. A. Hosseini Ravandi, “Spectral analysis of blacks,” Color Res. Appl. 37, 176–185 (2012).

[CrossRef]

K. Ansari, S. H. Amirshahi, and S. Moradian, “Recovery of reflectance spectra from CIE tristimulus values using a progressive database selection technique,” Color Technol. 122, 128–134 (2006).

[CrossRef]

F. Del Frate and G. Schiavon, “Non-linear principal component analysis for the radiometric inversion of atmospheric profile by using neural networks,” IEEE Trans. Geosci. Remote Sens. 37, 2335–2342 (1999).

[CrossRef]

L. T. Maloney, “Evaluation of linear models of surface spectral reflectance with small number of parameters,” J. Opt. Soc. Am. 3, 29–33 (1986).

[CrossRef]

S. Usui, S. Nakauchi, and M. Nakano, “Reconstruction of Munsell color space by a five layer neural network,” J. Opt. Soc. Am. A 9, 516–520 (1992).

[CrossRef]

F. Ayala, J. F. Echavarri, and P. Renet, “Use of three tristimulus values from surface reflectance spectra to calculate the principal components to reconstruct these spectra by using only three eigenvectors,” J. Opt. Soc. Am. A 23, 2020–2026 (2006).

[CrossRef]

S. Peyvandi and S. H. Amirshahi, “Generalized spectral decomposition: a theory and practice to spectral reconstruction,” J. Opt. Soc. Am. A 28, 1545–1553 (2011).

[CrossRef]

J. Romero, A. Garcia-Beltran, and J. Hernandez-Andres, “Linear bases for representation of natural and artificial illuminants,” J. Opt. Soc. Am. A 14, 1007–1014 (1997).

[CrossRef]

G. Ketschen and J. C. Golival, “Non-linear generalization of principal component analysis: from a global to a local approach,” J. Sound Vib. 254, 867–876 (2002).

[CrossRef]

G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Math. Control Signals Syst. 2, 303–314 (1989).

[CrossRef]

T. Harifi, S. H. Amirshahi, and F. Agahian, “Recovery of reflectance spectra from colorimetric data using principal component analysis embedded regression technique,” Opt. Rev. 15, 302–308 (2008).

[CrossRef]

J. B. Cohen, “Dependency of the spectral reflectance curves of the Munsell color chips,” Psychon. Sci. 1, 369–370 (1964).

G. Kerschen and J. C. Golinval, “Feature extraction using autoassociative neural networks,” Smart Mater. Struct. 13, 211–219 (2004).

S. H. Amirshahi and F. Agahian, “Basis functions of the total radiance factor of fluorescent whitening agents,” Text. Res. J. 76, 192–207 (2006).

[CrossRef]

A. Rayat, S. H. Amirshahi, and F. Agahian, “Compression of spectral data using Box-Cox transformation,” Color Res. Appl.; doi: 10.1002/col.21771, to be published. (First published online October 11, 2012.)

U. Kruger, J. Zhang, and L. Xie, “Developments and applications of non-linear principal component analysis—a review” [Online]. Available: http://pca.narod.ru/1MainGorbanKeglWunschZin.pdf .

S. Farajikhah, F. Madanchi, and S. H. Amirshahi, “Non-linear principal component analysis for compression of spectral data,” presented at IS2011, Ljubljana, Slovenia, 2011.

http://www.nlpca.org/matlab.html,19/5/2013 .

MATLAB, Version 7.8.0, The MathWorks Inc. (2009).