title: Principal component discriminant analysis.
abstract: The approach adopted involved two-stages. First the 11205 measurements in the mass spectrometry data were reduced to 14 scores by a principal component analysis of the centered but otherwise untreated and unscaled data matrix. Then a linear classifier was derived by linear discriminant analysis using these 14 scores as inputs. This number of scores was chosen by leave-one-out cross-validation on the training set, where it gave an overall error rate of 14%. Some indication of the information used in the classification may be obtained from an inspection of the coefficients of the linear classifier.
authors.affiliation: University College, London. tom@stats.ucl.ac.uk
authors.forename: Tom
authors.initials: T
authors.lastname: Fearn
mesh_terms: D016002:Discriminant Analysis; D006801:Humans; D016014:Linear Models; D013058:Mass Spectrometry / Q000706:statistics & numerical data*; D025341:Principal Component Analysis; D040901:Proteomics / Q000706:statistics & numerical data*; D019265:Spectroscopy, Near-Infrared / Q000706:statistics & numerical data