Principal Component Discriminant Analysis

Fearn Tom
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Fearn Tom: University College, London

Statistical Applications in Genetics and Molecular Biology, 2008, vol. 7, issue 2, 6

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.

Date: 2008
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DOI: 10.2202/1544-6115.1350

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