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Principal Component Discriminant Analysis
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Tom Fearn
Published/Copyright:
February 8, 2008
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.
Published Online: 2008-2-8
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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