Robust principal component analysis applications

In Chapter 11 some recent approaches for dealing with different aspects of the data reconciliation problem are discussed. A more general formulation in terms of a probabilistic framework is first introduced and its application in dealing with gross error is discussed in particular. In addition, robust estimation approaches are considered, in which the estimators are designed so they that are insensitive to outliers. Finally, an alternative strategy that uses Principal Component Analysis is reviewed. 

In the first mentioned type of application, electrophoretic data are subjected to exploratory analysis techniques, such as principal component analysis (PCA) (5-8), robust PCA (rPCA) (9-13), projection pursuit (PP) (6,14-18), or cluster analysis (8, 19, 20). They all result in a simple low-dimensional visualization of the multivariate data. As a consequence, it will be easier for the analyst to get insight in the data in order to see whether there is a given... 

Table 6.2 Diagnostic Pattern Recognition (DPR) in various applications (R-LDA, robust linear discriminant analysis LDA, linear discriminant analysis QDA, quadratic discriminant analysis RDA, regularised discriminant analysis ANN, artificial neural network PCA, principal component analysis SVM, support vector machine Nteach. number of teaching samples Npara, number of parameters used for classification (principal components etc)-, ratio, Nteach/Nparal Nvai, number of independent validation samples SE, sensitivity SP, specificity LOO, leave-one-out validation. AMI,... 

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