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.

Submitted: January 17, 2008 · Accepted: January 26, 2008 · Published: February 8, 2008

Recommended Citation

Fearn, Tom (2008) "Principal Component Discriminant Analysis," Statistical Applications in Genetics and Molecular Biology: Vol. 7 : Iss. 2, Article 6.
Available at: http://www.bepress.com/sagmb/vol7/iss2/art6