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Using discriminant analysis to estimate linear mixing proportions
Author(s) -
Burdick D. S.,
Rayens W. S.
Publication year - 1987
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.1180010304
Subject(s) - mixing (physics) , linear discriminant analysis , context (archaeology) , mathematics , component analysis , linear model , component (thermodynamics) , chromatography , statistics , pattern recognition (psychology) , chemistry , artificial intelligence , computer science , thermodynamics , physics , paleontology , quantum mechanics , biology
This paper proposes an elegant, yet straightforward, model for classifying linear mixtures. A linear mixture is defined as a random vector y in which the variable are a (non‐negative) weighted average of corresponding variables, assumed to characterize g component groups. These weights are referred to as ‘mixing proportions’. The model seeks to identify the mixture constituents and estimate the mixing proportions. It is demonstrated within the context of high resolution gas chromatography and the problem of identifying the constituents in polychlorinated biphenyl mixtures.