Robust linear discriminant analysis

Croux, C., Dehon, C. Can. J. Stat. 29, 2001, 473-492. Robust linear discriminant analysis using S-estimators. 

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,... 

It is commonly the case that a wide variety of properties can be included in a QSAR analysis and a decision must be made on whether to include all possibilities or limit the number of descriptors. This decision depends on the size of the data set and the correlation matrix between the properties. Farge sets of property data contain a lot of redundancy of information. For example, molecular weight, surface area and molar refraction are always highly correlated, therefore a decision to nse only molecular weight could be made. Some multivariate statistical analysis methods are tolerant of data sets which contain more properties than compounds, for example, PFS, while others are not, for example, linear discriminant analysis (FDA). Ideally, a set of uncorrelated properties is desirable as this is most likely to give a robust, interpretable model. 

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