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Principal Component Discriminant Analysis

Author

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  • Fearn Tom

    (University College, London)

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.

Suggested Citation

  • Fearn Tom, 2008. "Principal Component Discriminant Analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-6, February.
    • Handle: RePEc:bpj:sagmbi:v:7:y:2008:i:2:n:6
    DOI: 10.2202/1544-6115.1350
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    Cited by:

    1. Gutiérrez, Luis & Gutiérrez-Peña, Eduardo & Mena, Ramsés H., 2014. "Bayesian nonparametric classification for spectroscopy data," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 56-68.
    2. Hand David J, 2008. "Breast Cancer Diagnosis from Proteomic Mass Spectrometry Data: A Comparative Evaluation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(2), pages 1-23, December.

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