Independent Component Analysis and Nonnegative Linear Model Analysis of Illuminant and Reflectance Spectra
Xiong, W. and Funt, B., "Independent Component Analysis and Nonnegative Linear Model Analysis of Illuminant and Reflectance Spectrae",
AIC'2005 Proc. 10th Congress of the International Color Association, Granada, May 2005
Abstract:
Principal Component Analysis (PCA), Independent Component Analysis (ICA), Non-Negative Matrix
Factorization (NNMF) and Non-Negative Independent Component Analysis (NNICA) are all techniques that
can be used to compute basis vectors for finite-dimensional models of spectra. The two non-negative
techniques turn out to be especially interesting because the pseudo-inverse of their basis vectors is also close
to being non-negative. This means that after truncating any negative components of the pseudo-inverse
vectors to zero, the resulting vectors become physically realizable sensors functions whose outputs map
directly to the appropriate finite-dimensional weighting coefficients in terms of the associated (NNMF or
NNICA) basis. Experiments show that truncating the negative values incurs only a very slight performance
penalty in terms of the accuracy with which the input spectrum can be approximated using a finitedimensional
model.
Full text (pdf)
Keywords:
Colour vision, independent component analysis, modeling spectra, finite dimensional models, sensor design
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